What Is Customer Analysis? Methods, Models and a Step-by-Step Process

Customer analysis is the process of studying who your customers are, how they behave and why, so you can decide which customers to focus on, what to offer them and how to serve them better. It draws on two kinds of evidence: behavioural data, such as purchases, visits and usage, which shows what customers do; and customer feedback and research, which shows what they think and why. This guide explains the main methods and models, the data you need, a step-by-step process and the mistakes that make an analysis misleading.

What customer analysis answers

A good customer analysis answers four questions:

• Who are our customers? Their characteristics, such as age, location and life stage, or industry and company size for business customers.
• What do they buy, and how? Products, spend, how often they buy and which channels they use.
• Why do they choose us, stay or leave? Their needs, motivations, satisfaction and frustrations.
• Which customers matter most? Their value now, their potential and what it costs to serve them.

Customer analysis in marketing

In a marketing plan, customer analysis sits in the situation analysis, alongside analysis of the market, competitors and the company itself. Its findings feed segmentation, targeting and positioning: which customer groups exist, which ones to pursue, and how to present the offer so it appeals to them. Those choices then shape pricing, channels, messages and product decisions.

It is not only a marketing exercise: operations and service teams use the same evidence to find which locations, touchpoints or processes frustrate which customers.

Customer analysis methods and models

Most customer analyses combine several of the methods below. Each answers a different question:

Customer segmentation

Segmentation divides customers into groups that share characteristics or needs, so each group can be served differently. The common bases are:

• Demographic: age, gender, income, occupation, life stage.
• Geographic: city, region, urban or rural, the catchment of a branch or outlet.
• Behavioural: how often and how much customers buy, which products and channels they use, how loyal they are.
• Psychographic: values, interests, lifestyle and attitudes.
• Needs-based: the problem the customer is trying to solve.

Business customers are segmented by firmographics such as industry and company size. Demographics describe who customers are; behavioural and needs-based segments explain more about what they do and why, so the most useful segmentations combine them. A segment is worth acting on when it is large enough to matter, different enough to need its own approach and reachable.

Customer personas

A persona is a fictional but realistic profile of a typical customer in a segment: their goals, needs, frustrations, how they buy and which channels they prefer. Personas help teams keep a real customer in mind when making decisions, but they are only as good as the research behind them. The Nielsen Norman Group stresses that personas must be based on user research, so build them from interviews, surveys and behaviour data rather than from what the team assumes customers are like, and keep to a handful.

RFM analysis

RFM ranks customers on three measures taken from transaction history:

• Recency: how recently the customer last bought.
• Frequency: how often they buy.
• Monetary value: how much they spend.

Each customer gets a score for each measure, commonly from 1 to 5 by splitting customers into five equal-sized groups, and the scores are combined. Customers who score high on all three are your best customers; those with high past frequency and spend but low recency are valuable regulars who may be drifting away. RFM needs only billing data, so it suits any business with repeat purchases, but it says nothing about why customers behave as they do. A related measure, customer lifetime value (CLV), estimates the total revenue or profit a customer will bring over the whole relationship.

Customer journey analysis

Journey analysis follows the steps a customer takes to reach a goal: finding you, choosing, buying, using the product or service, getting help, and coming back or leaving. A journey map lays out what the customer does, thinks and feels at each stage, and marks pain points and opportunities. The evidence comes from website or app funnels, feedback collected at each touchpoint (at checkout, after billing, after a service visit), complaints and staff observations. Our guide to customer experience journey maps shows how to build one.

Voice of the customer and feedback analysis

Voice of the customer (VoC) is what customers say about their needs, expectations and experience. The quality body ASQ defines it as the expressed requirements and expectations of customers relative to products or services. Sources include surveys such as CSAT surveys and Net Promoter Score, comment cards and QR feedback forms, online reviews, complaints and support conversations.

Feedback analysis turns this into evidence you can act on:

• Track scores over time and compare them by segment, location and touchpoint.
• Sort open comments into themes, such as wait time, staff behaviour, price or cleanliness, and count how often each appears.
• Note the sentiment of comments, and which themes come up most in low ratings.
• Prioritise themes by how often they occur and how much they affect the customers you most want to keep.

Cohort analysis

A cohort is a group of customers who share a starting event in the same period, such as everyone who made a first purchase, opened an account or signed up in a given month. Google Analytics, for example, groups users into cohorts by acquisition date. Cohort analysis tracks each group over time, for instance how many are still buying after one, three and six months, so you can see whether retention is improving and whether a change, such as a new onboarding process or a price rise, affected the customers who joined after it. A segment groups customers by who they are or what they do; a cohort groups them by when they started.

Data sources for customer analysis

• Transaction data: billing and point-of-sale records, orders and invoices.
• CRM and account records: contact history, service requests and account details; see our overview of types of CRM.
• Website and app analytics: visits, searches, conversion paths and drop-off points.
• Customer feedback: survey ratings and comments, point-of-service feedback, complaints and online reviews, which differ from feedback in useful ways.
• Service records: call-centre logs, support tickets, returns and refunds.
• Frontline staff: what customers ask about and complain about in person.
• Research: interviews, focus groups and surveys designed for the question, plus industry reports and government statistics.

Customer records that identify people are personal data under India’s Digital Personal Data Protection Act, 2023. Tell customers what you collect and why, limit data to what that purpose needs, keep it secure and erase it once the purpose is served. Where you can, analyse aggregated or de-identified data.

Where customer feedback fits, and its limits

Behavioural data tells you what happened: regulars stopped visiting, or spend in one segment fell. Feedback often tells you why: slow billing, an unhelpful interaction, a product out of stock. The two are strongest together:

• It explains the numbers. When RFM shows regulars drifting away from one outlet, comments from that outlet can point to the cause.
• It flags problems early. A run of poor ratings at one branch is a reason to investigate before the problem shows up in sales.
• It shows where to act. Feedback tagged by location, department or touchpoint points to the specific place that needs attention.

Feedback also has limits that an honest analysis allows for:

• Respondents choose themselves, so they may not represent all your customers.
• Saying and doing differ. “I will come back” is not a repeat visit.
• Small samples swing. A handful of responses from one segment can move a score sharply.
• Lost customers rarely reply, so also contact customers who have lapsed.

Use feedback to explain and prioritise, and check it against behavioural data before big decisions. If you collect feedback with LazyMonkey’s customer feedback management software, responses can be filtered by location, department or product category and compared across customer types such as new, returning and VIP customers, which makes it easier to line feedback up with the segments in your analysis. For the wider process, see what customer feedback management involves.

How to do a customer analysis, step by step

1. Start with a business question, such as “Why are repeat visits falling at some outlets?” or “Which customers should a loyalty programme target?” The question decides the data and methods.
2. Gather and clean the data. Combine transaction, CRM, analytics and feedback data, match records to the same customer or location, and remove duplicates.
3. Segment customers, starting simply: new versus repeat, by location or by RFM group.
4. Measure value and behaviour with RFM, lifetime value or cohort retention to see which segments are growing, valuable or slipping away.
5. Add the why. Read feedback from the segments that matter, and run a few interviews where data is thin.
6. Summarise the findings, as personas or journey maps if other teams need to act on them.
7. Act and test. Agree a few changes, each with an owner. Where possible, try a change in one segment or location first and compare it with a similar group.
8. Repeat, so the analysis keeps up as customers change.

What to include in a customer analysis report

• The question, the period covered and the data sources used.
• The customer segments, with the size and value of each.
• Key behaviour findings, such as RFM groups, cohort retention and journey drop-off points.
• What customers say: the main feedback themes for each segment and how scores have moved.
• Recommended actions, each with an owner and a measure of success.
• The limits of the data, such as gaps, small samples or response bias.

Keep it short: a few pages of clear findings get acted on; a long data dump does not.

Common pitfalls

• Starting with data instead of a question, which produces charts but no decisions.
• Relying on averages. An overall score can hide one segment that is unhappy.
• Confusing correlation with causation. Customers who use a feature may spend more because they were already your best customers, not because of the feature.
• Ignoring customers who left. Their reasons rarely show up in feedback from current customers.
• Letting the analysis go stale while customers and competitors change.

Frequently Asked Questions