Customer Analysis

Customer analysis is the systematic process of identifying who a market's actual and potential buyers are, grouping them into segments that behave similarly, and characterizing what each group needs, how it decides to buy, and how valuable it is over time (historically and prospectively), so that product, pricing, messaging, and resource-allocation decisions can be built around observed buyer behavior rather than assumption.

It sits alongside, and feeds, market segmentation and the wider Segmentation-Targeting-Positioning (STP) sequence that Philip Kotler and Kevin Lane Keller describe in *Marketing Management*: segmentation identifies distinct groups of buyers who differ in their needs and wants, targeting selects which groups to pursue, and positioning defines how the offer will be presented to them. Customer analysis is the underlying research discipline that makes each of those three steps evidence-based: it supplies the needs data segmentation clusters on, the purchase-criteria data targeting weighs, and the perception data positioning is built against.

Modern customer analysis combines qualitative frameworks that explain *why* a customer buys, most notably Jobs to Be Done (the theory, developed by Tony Ulwick from 1990 and popularized by Clayton Christensen in his 2003 book *The Innovator's Solution*, that a customer 'hires' a product to get a specific job done in a specific circumstance) with quantitative frameworks that measure *how valuable* a customer is, most notably RFM analysis (Recency, Frequency, Monetary value, a database-marketing scoring technique) and Customer Lifetime Value (CLV, the total net value a business can expect from an account over the life of the relationship).

In scope: profiling who a market's buyers are (demographic, firmographic, or behavioral traits); identifying the underlying job, need, or problem each buyer group is trying to solve; mapping the criteria buyers actually use to choose between offers (price, quality, convenience, brand, service, switching cost); scoring buyers' historical value and purchase behavior (RFM); and projecting a buyer's future value to the business (CLV). Customer analysis is an ongoing research discipline, re-run as behavior, technology, and competitive offers shift, not a one-time deliverable.

Out of scope / commonly confused with:
- Customer analysis is not market segmentation. Segmentation is the specific act of clustering buyers into named groups using chosen variables; customer analysis is the broader research process (needs, purchase criteria, JTBD, valuation) that supplies segmentation with the data it clusters on and that continues to apply *within* a chosen segment even after clustering is done. See the market-segmentation method for the clustering step itself.
- Customer analysis is not competitive analysis. Understanding what your buyers want is a different question from understanding what alternatives they are comparing you against (see the competitive-analysis method).
- Customer analysis is not market sizing. Knowing who your buyers are and how much a given customer is worth (CLV) is not the same calculation as knowing the total addressable market in dollars (see the market-size and TAM/SAM/SOM methods) -- CLV and RFM operate at the individual-customer or cohort level, market sizing operates at the total-market level.
- Customer analysis is not a satisfaction survey. Net Promoter Score, CSAT, and similar sentiment metrics measure how buyers feel about an existing relationship; customer analysis is the broader diagnostic of who they are, what they need, and what they are worth, of which sentiment is only one input.

• Before writing a go-to-market plan or product roadmap, to establish who the actual buyers are and what job they are hiring the product to do, rather than assuming the team already knows.
• When customer acquisition cost (CAC) is rising or conversion is falling and the cause is unclear -- customer analysis tests whether the offer still matches what the target buyer actually needs and how they decide.
• When deciding how much to spend to acquire or retain a given type of customer -- Customer Lifetime Value gives a ceiling for sustainable CAC (CLV should exceed CAC by a healthy multiple, commonly cited around 3x, for the relationship to be worth acquiring).
• When a retention or lifecycle-marketing program needs to prioritize which existing customers to target first -- RFM scoring ranks the existing base by recent, frequent, high-value behavior so limited retention budget goes to the customers most likely to respond and most valuable if retained.
• When a product team is deciding what to build next and needs to distinguish the underlying job customers are trying to accomplish from the specific product features they currently use to accomplish it (Jobs to Be Done separates the two, since the job is stable even when the products used to do it change).
• Before commissioning primary market research (surveys, interviews), so the research is designed to test a specific hypothesis about buyer needs or purchase criteria rather than collected blind.

1. Define the buyer population to be analyzed and its boundaries (existing customer base, a target market, or both), consistent with the scope of the segmentation or market-sizing exercise it will feed.
2. Gather behavioral and transactional data on that population: purchase history, product usage, support interactions, and (for existing customers) recency/frequency/monetary detail per account.
3. Run RFM scoring on the existing customer base: rank or bucket each customer on Recency (how long since last purchase), Frequency (how many purchases in a defined period), and Monetary value (total or average spend in that period), then combine the three scores into a small number of behavioral tiers (e.g. 'champions', 'at risk', 'lapsed').
4. Conduct qualitative research (customer interviews, win/loss interviews, support-ticket analysis) to identify the underlying Jobs to Be Done: the functional, social, and emotional progress each buyer type is trying to make, independent of the specific product they currently use.
5. Map purchase criteria: for each buyer type, the ranked list of factors (price, quality, speed, service, brand, switching cost, risk) that actually drives the choice between competing options, distinguishing stated criteria (what buyers say matters) from revealed criteria (what their actual purchase behavior shows matters).
6. Calculate Customer Lifetime Value for each meaningful buyer group, using the formula and inputs below, to quantify how much the business can justify spending to acquire and retain each group.
7. Synthesize the RFM tiers, JTBD findings, purchase criteria, and CLV figures into buyer profiles that feed directly into segmentation, targeting, product prioritization, and retention-budget decisions.
8. Re-run the analysis on a fixed cadence (commonly annually for consumer categories, on a longer cycle for slow-moving B2B/industrial categories) since buyer behavior, competitive offers, and the jobs being done all drift over time.

Illustrative example only: all figures below are demo numbers chosen to demonstrate the method, not sourced market data.

A subscription software business wants to know how much it can afford to spend acquiring a new customer in its mid-market segment.

RFM step: it pulls its existing mid-market customer base and scores each account 1-5 on Recency, Frequency (renewals in the last 24 months), and Monetary value (total spend). A customer that renewed last month (Recency=5), has renewed 4 times (Frequency=4), and has spent $19,200 to date (Monetary=5) gets an RFM code of 5-4-5, placing it in the top 'champions' tier the retention team prioritizes first.

CLV step, using the SaaS formula: Average Revenue Per User = $400/month; Gross Margin = 80%; Churn Rate = 2.5%/month (implying an average lifespan of 1 / 0.025 = 40 months).

CLV = ($400 x 0.80) / 0.025 = $320 / 0.025 = $12,800

With CLV of $12,800, and applying the commonly cited guideline that CLV should exceed Customer Acquisition Cost (CAC) by roughly 3x for a healthy unit economics profile, this segment can sustainably support a CAC of up to approximately $12,800 / 3 = $4,267. If the business's actual blended CAC for this segment is, say, $2,500, the segment is comfortably above the 3x threshold and a strong candidate to prioritize for further acquisition spend. Every input above (ARPU $400, margin 80%, churn 2.5%, CAC $2,500) is an illustrative assumption for this worked example, not a researched figure, and would need to be replaced with the business's own actual billing, cost, and churn data before being used to guide a real budget decision.

Not yet available.

• Philip Kotler and Kevin Lane Keller, *Marketing Management* -- the standard reference for the Segmentation-Targeting-Positioning (STP) sequence that customer analysis feeds.
• Tony Ulwick / Strategyn, 'The History of Jobs to Be Done: How Tony Ulwick Created JTBD' -- the originating account of the Jobs to Be Done methodology, developed from 1990.
• Clayton M. Christensen, *The Innovator's Solution* (Harvard Business School Press, 2003) -- the book that popularized Jobs to Be Done theory, including the milkshake example.
• Harvard Business Review, 'What Most Companies Miss About Customer Lifetime Value' (Michael Schrage, April 2017) -- on the strategic use and common misuse of CLV.