Chapter 9
Marketing Tools
1 Introduction
Chapter 8 introduced marketing: what it is, the marketing environment, segmentation and targeting, and the marketing mix. This chapter completes the picture with the main tools marketers use in practice – marketing research, big data analytics, channel management, sales forecasting, brands, the product life cycle and the BCG matrix.
Keep the syllabus lens in mind throughout. E1 is not training you to be a marketer: it asks how the finance function interacts with sales and marketing – where the two functions touch, and which key performance indicators (KPIs) they share. Marketing decisions are also finance decisions: every campaign, channel and price point shows up in revenue, margin and cash flow. The final section of this chapter pulls the finance interface and the marketing KPIs together.
This lecture was recorded under the previous syllabus. The content remains a good foundation, but note: channel management and sales forecasting and management are not covered in the lecture – see those sections in these notes; big data analytics appears only through the loyalty-card story – see 'Big data analytics in marketing'; the finance interface and marketing KPIs (CAC, CLV, churn, ROMI) are new – see 'How finance interacts with sales and marketing'; and field research is now mostly online (surveys, social listening, A/B testing) rather than street questionnaires.
2 Marketing research
2.1 Introduction
Good marketing depends on good information, and that information comes from marketing research. Who are our customers? What do they want, need, appreciate and respond to? Information – not guesswork – is needed on every element of the marketing mix.
(Strictly, market research just means finding out about the market – its size and who buys. Marketing research is wider: it covers every aspect of marketing activity.)
There are three types of marketing research:
Desk research
Field research
Test markets
2.2 Desk research
Desk research uses information that has already been collected. It is usually relatively quick and cheap, but it might not answer all of your questions.
Sources of information include:
Internal – the accounting system. The accounting department can supply enormous amounts of information, provided transactions were properly coded in the first place. If sales are analysed by product, region and channel rather than posted to a single sales account, the business can see which products are growing or declining, seasonal patterns, and responses to competitor action.
Internal – the data warehouse and web analytics. Modern businesses accumulate far more than accounting data: website and app analytics (visits, searches, abandoned baskets), customer-relationship-management (CRM) records and loyalty-scheme transaction histories. This is the raw material for the big data analytics covered in the next section.
External – governments, national and local. Censuses and official statistics reveal population sizes and age profiles, incomes and inflation. A publisher of educational materials, for example, needs to know whether the school-age population is rising or falling; local population data helps a supermarket chain decide where to open a store.
External – market research firms and published data. Research firms routinely collect industry information in the hope of selling it: new-car registrations, brand-share trackers, consumer-panel data. A car manufacturer can buy insight into competitors' sales and customer profiles. A great deal of competitor information is also freely available online – prices on competitors' websites, product reviews, and comments on social media (monitoring these is called social listening).
2.3 Field research
Field research means going out and collecting specific, first-hand information:
Surveys and questionnaires. Once conducted by stopping people in the street, these are now mostly run online or by email, which makes them far cheaper and faster to distribute and analyse. Questions might cover buying habits, advert recall, brand awareness or reactions to a proposed marketing initiative.
Focus groups and panels. Small groups of consumers discuss a product or idea between themselves while the researcher observes and records the discussion – richer, qualitative insight than a questionnaire can provide.
Product testing. New products – food, for instance – are given to households to use, and opinions are collected a few weeks later. Testing for safety and durability is done in the laboratory.
2.4 Test markets
The final piece of marketing research comes just before launch. Rather than launching nationally or internationally at once, the product is tried out in a relatively small area. A good test market is:
Small – so the trial is affordable
Representative of the full customer population
Stable in population (a university town is often unsuitable because its population changes radically between terms)
Equipped with suitable facilities – if the plan is to sell through supermarkets and advertise on local radio, the test market must have both.
The company watches the launch carefully, carrying out further research as it goes. The test market is the last chance to get things right before the expense of a national launch. A failed launch loses money, damages reputation, and can make it harder to enter that segment of the market in future.
Online sellers have a digital equivalent of the test market: A/B testing. Two versions of a web page, price or advert are shown to different random samples of visitors and the results are compared. It is fast, cheap and continuous – digital businesses effectively run test markets every day.
3 Big data analytics in marketing
Chapter 4 explained big data and data analytics in general. Marketing is one of their most important applications, and the syllabus names big data analytics in marketing as a specific topic.
Marketing generates enormous volumes of data: every loyalty-card transaction, website visit, search, click, review and social-media mention. Analysing it lets marketers:
Understand and segment customers. Transaction histories reveal shopping patterns, seasonality and unexpected correlations between products – which can drive store layouts, promotions and product bundling.
Personalise. Recommendation engines ('customers who bought this also bought…'), personalised offers and targeted advertising are all driven by analysing individual customer data.
Predict. Predictive models estimate which customers are most likely to respond to a campaign – and which are about to defect to a competitor (churn prediction), so they can be targeted with retention offers.
Price dynamically. Airlines, hotels and online retailers adjust prices continuously in response to demand, capacity and competitors' prices.
Measure what works. Digital marketing is unusually measurable: the business can trace which advert a customer clicked and what they then bought (attribution), and so calculate the return on its marketing spend far more precisely than was ever possible with television or print advertising.
Generate content. Generative AI now drafts advertising copy, product descriptions and campaign variants, which are then A/B-tested (see the aside above) to see which performs best.
A supermarket issues loyalty cards. Customers earn points, but the real prize is the data: every purchase by every cardholder is recorded and kept for years in the data warehouse. Data mining that history uncovers patterns – seasonality, product correlations, price sensitivity – and supports personalised voucher campaigns. The supermarket can measure the campaigns' success directly, because it sees exactly who redeemed each voucher and what else they bought.
Finance has a direct stake in all of this: it helps to build the business case for investment in data platforms, checks that claimed benefits actually materialise, and – because customer data is heavily regulated (data-protection law) and easily misused – shares responsibility for its governance and ethical use (see Chapters 3 and 11).
The ethics and law of data-driven marketing matter. Data protection law (in the UK, the UK GDPR and the Data Protection Act 2018, as amended by the Data (Use and Access) Act 2025) requires a lawful basis for processing personal data and transparency about its use. Essential and certain limited low-intrusion cookies can be set without consent, but advertising and cross-site tracking still generally require it. Beyond compliance, excessive tracking, manipulative personalisation and biased algorithms can destroy the customer trust marketing depends on – an application of the data-ethics principles from Chapter 4.
4 Business-to-Consumer, Business-to-Business and Business-to-Government marketing
The group to whom marketing effort is addressed affects the approach taken:
Market | Typical characteristics |
Business to consumer (B2C) | Buyers are relatively inexpert, so they are susceptible to promotional messages. Promotion is typically through television, online advertising and social media. Products are used domestically, so styling suits the home and items need not be as sturdy as business equipment. Small quantities are bought, online or from retail outlets. |
Business to business (B2B) | Buyers are expert and bargain hard for discounts and lower prices. Promotion is typically through catalogues, trade shows, online channels and visiting sales representatives. Quality is often critical, and styling can be functional. Orders are large, so key customers must be identified and looked after carefully. |
Business to government (B2G) | Orders can be huge and prices reflect this. Purchasing is often through formal, competitive tendering processes, so specialised negotiation and bidding skills are needed. Decisions are slow, so patience is required. |
5 The marketing of services and products compared
What a service business provides differs from what a manufacturer provides in five classic respects – and each difference changes the information the business needs:
Characteristic | What it means |
Heterogeneity | Manufacturing produces many identical units; services are often tailored to each customer (an audit, for example). Costing, efficiency measurement and pricing are all harder, because customers find it more difficult to judge prices. |
Perishability | Many services lose their value after a certain time. Once the aircraft departs, unsold seats are worth nothing. This creates interesting pricing challenges: each extra passenger should be attracted at the maximum marginal price, but if everyone learns that prices fall near departure, customers postpone booking. |
Intangibility | It is difficult to show a potential customer what they will get for their money. An audit firm cannot demonstrate an audit in advance, so how do potential clients judge value for money? Reputation and word of mouth become critical. |
Simultaneity | In manufacturing, production and sale can be separated: products are quality-checked before dispatch, and inventory can be built up steadily and stored for busy periods. Services cannot be stored and are usually produced and consumed at the same moment – placing extra demands on scheduling, pricing and quality control. |
No transfer of ownership | A service is often the use of something for a limited period (a hotel room, a streaming subscription). Pricing and demand information must reflect this – hotel-room prices vary between weekdays and weekends – and customers are demanding during the period they are paying for. |
The information needed to run a service business well is therefore often more qualitative than quantitative: reputation, customer satisfaction and the availability of the service when required.
6 Relationship marketing
Relationship marketing emphasises customer satisfaction and retention, rather than living from one sales transaction to the next. The aim is to turn a casual customer into a client (a repeat customer) and eventually into an advocate who recommends the company to others.
It is powered by CRM (customer relationship management) systems, which track and analyse each customer's purchases, preferences, service history, conversations and complaints. A car manufacturer that knows when and how its customers buy, which options they choose and how they finance the purchase is in a powerful position to make one-to-one offers – for example, contacting a customer as their finance agreement nears its end. Every website visit, app session and email interaction adds to the relationship data.
Retention matters financially: winning a new customer usually costs far more than keeping an existing one, and long-standing customers tend to buy more and cost less to serve. This is the thinking behind the customer lifetime value KPI covered at the end of this chapter.
7 Experiential marketing
Experiential marketing (also called engagement, event or live marketing) directly engages consumers and invites them to participate in an experience built around the brand. The objective is to create a closer bond between consumer and brand through a fun, exciting and memorable event – a pop-up shop, a festival sponsorship, an in-store event or an immersive product demonstration. Participants often share the experience on social media, extending its reach far beyond those physically present.
For the exam you need only the idea: it is background to the main E1 question of how marketing activity is planned, measured and paid for.
8 Channel management
A distribution channel is the route by which products or services reach the customer. Channel management – choosing, combining and controlling those routes – is a named topic in the 2027 syllabus.
8.1 Types of channel
Direct channels. The producer sells straight to the customer: its own stores, its own website, its own sales force. The producer keeps the whole margin and controls the customer experience, but bears all the selling costs and must attract customers itself.
Indirect channels. The product passes through intermediaries – wholesalers, distributors, retailers, agents or online marketplaces. Intermediaries provide reach, local knowledge and bulk-breaking, but each takes a share of the margin, and the producer loses some control over pricing, presentation and customer data.
8.2 Channel developments
Multi-channel and omni-channel. Most businesses now sell through several channels at once. Omni-channel means integrating them so the customer experiences one seamless business: order online and collect in store, return an online purchase to a branch, start a query in an app and finish it by phone. Integration demands connected systems – and consistent prices and stock data – across every channel.
Disintermediation. The internet lets producers cut intermediaries out and deal directly with customers: airlines sell seats from their own websites rather than through travel agents; musicians release music directly to streaming platforms. The producer captures the intermediary's margin but takes on the intermediary's work.
Reintermediation. New digital intermediaries have appeared in their place – comparison sites, online marketplaces and aggregators – which charge commissions and change the economics of the channel again.
Channel decisions create channel conflict risks – retailers resent a supplier's direct online store undercutting them – and management means setting consistent pricing rules and deciding which products go through which channels.
Finance cares about channels because different channels earn different margins. Analysing profitability by channel – after commissions, delivery, returns and channel-specific promotion – is exactly the kind of insight the finance function provides to marketing. A sale through a marketplace that charges 15% commission is a very different sale from the same item sold through the company's own website.
9 Sales forecasting and management
9.1 Sales forecasting
The sales forecast is the foundation of almost every other plan in the organisation. Production capacity, purchasing, staffing, cash-flow forecasts and the master budget all start from expected sales – so finance and marketing must work from one agreed forecast, not two competing ones.
Forecasting methods include:
Judgement and sales-force estimates. Sales representatives estimate what their own customers will buy; management combines and moderates the estimates. Close to the customer, but often biased – especially where targets or bonuses depend on the answer.
Market research based. Estimates built from market size, expected market share and buying intentions gathered by the research methods earlier in this chapter. Essential for new products with no sales history.
Statistical and time-series analysis. Extrapolating past sales, allowing for trend and for seasonal variation. Reliable for established products in stable markets – and useless when the pattern breaks.
Data-driven predictive models. Machine-learning models that combine sales history with other data – prices, promotions, weather, economic indicators, web traffic – to predict demand. Widely used by retailers for store- and product-level forecasting.
Finance and sales share responsibility for making the forecast honest: sales teams may forecast optimistically (to please) or pessimistically (to make targets easy to beat). Finance tests forecasts against history and market evidence, and measures forecast accuracy itself as a KPI – a persistently wrong forecast means wrong production, wrong inventory and wrong cash planning.
9.2 Sales management
Sales management turns the forecast into results: setting sales targets and territories, managing the pipeline of potential deals recorded in the CRM system, motivating the sales force (commission and incentive schemes – see the reward material later in Chapter 10), and monitoring performance against target so that a shortfall is spotted while there is still time to act.
Incentive design is a joint exercise with finance: commission structures should reward profitable sales (margin, cash collected) rather than pure volume, otherwise the sales force will happily give away discounts and extended credit terms that destroy margin. Finance also monitors the discounting authority and credit limits granted by sales staff.
If the November forecast is 10,000 units and the CRM pipeline shows committed orders for only 6,000 by mid-October, sales management can act now – extra promotion, price incentives, redeploying representatives – and finance can flag the revenue and cash-flow impact of the likely shortfall to the board. The forecast, the pipeline and the budget only work when marketing and finance share them.
10 Brands
A brand is a unique design, sign, logo, symbol (or combination of these) used to create an image that identifies a product and differentiates it from competitors.
Over time, successful brands become associated with desirable qualities such as quality, reliability, price or taste. This lets consumers identify and buy products they like and trust quickly: supermarket shelves are crowded with competing products, but shoppers simply grab the familiar packaging without much thought – because the brand has pleased them before.
Brand owners work hard to associate a particular image with their brands (up-market or value, for example) and defend that image strongly – some manufacturers allow their luxury brands to be stocked only by exclusive outlets.
Brand value (or brand equity) is the additional income a company can earn from a branded product compared with its generic equivalent. It can be enormous – and it can be destroyed. If consumers would actually pay more for the generic product, the brand has negative equity, which can happen after a major scandal: Volkswagen's brand was seriously damaged by the 2015 diesel-emissions scandal, and the company paid out tens of billions in fines and settlements.
For finance, brands are awkward assets: internally generated brands do not appear on the statement of financial position, yet they may be the most valuable thing the company owns, and marketing spend to build them is expensed as incurred. This is one reason marketing budgets need non-financial KPIs (brand awareness, market share) alongside financial ones.
11 The product life cycle
11.1 Introduction
The product life cycle is a well-known diagram which purports to show how sales revenue and profit change as a product moves from introduction through growth to maturity and then decline:
The problem with the diagram is that no product is guaranteed to follow this pattern, and even when one does, the phases vary enormously in length – maturity can last decades for some products and a few years for others. What managers would really like to know is when irrevocable decline sets in, and the diagram cannot predict that. What it does provide is a useful set of labels.
11.2 The phases explained
Introduction. Watch sales very carefully to judge whether the product is likely to succeed. Sales are low, launch costs are high, and the product is loss-making.
Growth. Success attracts competitors into the market. Keep promoting strongly to stay ahead of the field.
Maturity. Many suppliers; buyers are well informed and demanding. Price pressure builds – extreme where the industry has over-capacity.
Decline. Be careful not to misread a temporary dip as the start of decline. Relatively cheap upgrades and facelifts can extend life for years – and profitably, because development costs and much of the machinery have already been paid for. A strategic choice must be made: exit quickly, or be the last player standing and enjoy a near-monopoly of a declining market.
11.3 Life cycle costing
Whatever shape the life cycle takes, every product has costs before, during and after production:
Initial costs – before production (research, development, tooling)
Operating costs – during production
Disposal costs – after production (decommissioning, environmental clean-up)
Life cycle costing recognises that, for a profit to be made, revenue must cover all of these costs – not just the production costs.
Initial research and development costs $2m. 100,000 units will then be made, each costing $10 to manufacture. Demolishing the factory at the end of its life will cost $0.5m.
Total life cycle cost = $2m + ($10 × 100,000) + $0.5m = $3.5m
Life cycle cost per unit = $3.5m ÷ 100,000 = $35
The selling price must exceed $35 per unit, on average over the product's life, for the product to be profitable at all – even though the cost accounts during production show a unit cost of only $10.
12 The Boston Consulting Group (BCG) matrix
The BCG matrix is another well-known analysis, sometimes called portfolio analysis – it makes most sense when a company has more than one product or product line. The axes are relative market share and market growth rate:
Taking each quadrant in turn:
Question mark (problem child). High market growth but low market share. There is no long-term future for a product with only a small share – rivals with large shares enjoy far greater economies of scale – so the question is: get out, or invest to grab a large share? Going for share means heavy spending on promotion, development and price cuts: strongly negative cash flow and accounting losses.
Star. The question mark that succeeded: high share in a high-growth market. Not as comfortable as it sounds – a growing market attracts competitors who all want what you have, so heavy spending continues to defend the position. Cash flow is typically around zero.
Cash cow. Market growth eventually slows, and the high-share product becomes a cash cow. The initial expenses are long written off, and competitors no longer fight hard for share of an old product. The business enjoys high cash inflows without heavy spending – the emphasis switches to cost control to maximise profit.
Dog. Low share of a low-growth market – and note that cash cows do not turn into dogs. There is no point spending time and money building share in an old product: divest – close the production facilities or sell them.
Finally, the point of the word 'portfolio': a company with nothing but question marks has a financing problem – everything it owns consumes cash. A company with nothing but cash cows is comfortable today, but in a few years the cows decline and nothing replaces them. A well-balanced portfolio uses the cash generated by the cash cows to fund the question marks, securing the long-term future.
Notice that the BCG matrix is really a cash-flow analysis – which is why finance people like it. In the exam, be able to state each quadrant's typical cash position: question marks consume cash, stars are roughly neutral, cash cows generate it, dogs should be divested.
13 How finance interacts with sales and marketing
This is the heart of syllabus section E1E2. The main role of sales and marketing is to understand customers and markets, and to win and keep profitable sales. Finance interacts with it at every step:
Area of interface | What finance contributes |
Marketing budgets | Setting and monitoring the marketing budget; authorising campaign spend; comparing actual spend and results against plan. |
Pricing | Cost information for pricing decisions (including life cycle costs); margin analysis; evaluating discount structures and dynamic-pricing policies. |
Sales forecasting | One agreed sales forecast feeding the master budget, production plans and cash-flow forecasts; challenging optimism bias in sales estimates. |
Channel decisions | Profitability analysis by customer, segment and channel after commissions, delivery, returns and promotion costs; business cases for opening or closing channels. |
Campaign and data investment | Appraising investment in campaigns, CRM and data platforms; measuring return on marketing investment (ROMI); verifying that promised benefits materialise. |
Credit and revenue | Credit terms for customers won by the sales force; ensuring revenue is genuine and collectable, not just invoiced (sales commissions can tempt overselling to poor credit risks). |
Sales incentives | Designing commission and bonus schemes that reward profitable sales, and monitoring them for unintended behaviour. |
13.1 Marketing KPIs
The syllabus asks how KPIs shape the interaction between finance and marketing, and how the two functions' KPIs can be aligned. The key marketing KPIs to know:
KPI | What it measures |
Customer acquisition cost (CAC) | Total sales and marketing cost of winning one new customer. Should be compared with what the customer is worth (CLV). |
Customer lifetime value (CLV) | The total profit expected from a customer over the whole relationship. Justifies spending more to win and keep valuable customers; CLV should comfortably exceed CAC. |
Conversion rate | The proportion of prospects (or website visitors) who become buyers. Measures the effectiveness of each stage of the sales funnel. |
Churn / retention rate | The proportion of customers lost (or kept) in a period. Central to relationship marketing and to subscription businesses. |
Market share | The company's sales as a proportion of the total market – the axis of the BCG matrix, and a measure of competitive success that pure revenue growth can hide. |
Return on marketing investment (ROMI) | Profit generated per $ of marketing spend. Digital attribution makes this far more measurable than it used to be. |
Sales forecast accuracy | How close sales forecasts were to actual sales. Bad forecasts corrupt every budget built on them (see the sales forecasting section above). |
Net promoter score (NPS) | How likely customers are to recommend the company – a leading indicator of retention and future sales. |
The most examinable pairing is CAC against CLV: marketing wants to spend to win customers; finance asks whether the lifetime profit from those customers exceeds what it cost to win them. Alignment matters. If marketing is judged only on revenue or lead volume while finance is judged on margin and cash, the functions pull in different directions – marketing wins unprofitable customers and finance blocks worthwhile campaigns. Shared KPIs (CLV against CAC, ROMI, margin by channel) built on shared data give both functions the same definition of success.
Marketing research (desk, field, test markets) and big data analytics tell the business what customers want and what works. Channels determine how products reach customers and at what margin; the sales forecast drives every other budget. Brands, the product life cycle and the BCG matrix link marketing position to cash flow. Finance interacts with sales and marketing through budgets, pricing, forecasting, channel profitability and investment appraisal – and the two functions are aligned through shared KPIs such as CAC, CLV, conversion, churn, market share, ROMI and forecast accuracy.
14 Test your knowledge
Two quick checks before you move on: work through the flashcards to fix this chapter’s key terms and definitions, then sit the objective questions for exam-style practice. Both mark themselves and explain the answers as you go.
Marketing Tools
22 questionsAnswer the questions one at a time. Your progress is saved so you can leave and come back.
Open chapter practice