Approaches to marketing strategy

A marketing strategy is the broad, long-term plan a business uses to achieve its marketing objectives (1), including which markets to target and how to position its product (1).

A marketing strategy should match the nature of the product because the product determines the appropriate positioning. A luxury product, for example, needs a strategy of premium pricing, selective distribution and prestige promotion; selling it cheaply through discount channels would contradict its image and confuse customers. Consistency between the strategy and the product therefore keeps the brand's positioning credible and avoids wasting marketing spend on a mismatched approach.

The first benefit is personalisation. AI uses data about each customer's behaviour to recommend relevant products and tailor offers, so customers see what they are most likely to want. This raises conversion rates and sales because the marketing feels relevant rather than generic.

The second benefit is more precise, automated targeting through programmatic advertising. AI buys and places online ads in real time, aimed at the specific audiences most likely to respond, so less of the budget is wasted on uninterested viewers. This lowers the cost per sale and makes marketing spend more efficient.

Together, personalisation and precise targeting can increase sales while reducing cost, although the business must manage data responsibly to protect customer privacy.

A coordinated marketing strategy means all marketing activities — and the elements of the marketing mix — are integrated so they reinforce each other rather than working in isolation.

The first benefit is a clear, consistent brand image. When the 4Ps agree — a premium price supported by a high-quality product, selective distribution and prestige promotion — and all channels (website, social media, stores) send the same message, customers receive one coherent positioning. This makes the brand credible and memorable, and customers are more willing to pay the price the positioning implies.

The second benefit is more efficient use of the marketing budget. Coordinated activities reinforce one another, so the business avoids spending on contradictory campaigns that cancel each other out — for example a luxury image undermined by selling through discount stores. Each dollar of marketing therefore works harder towards the same objective.

The third benefit is alignment with other functions. Coordination ensures operations can supply the demand the marketing creates and finance can fund the activity, so the strategy is deliverable rather than creating demand the business cannot meet.

Therefore coordination makes a marketing strategy more effective: a consistent image, efficient spending and a deliverable plan all increase the chance of achieving the marketing objectives. The benefit is greatest where a business runs many activities across several channels, since that is where the risk of inconsistency is highest.

Investing heavily in AI could transform the retailer's marketing. AI enables personalisation, recommending products to each customer from their data, which raises relevance, conversion and sales. It allows programmatic, targeted advertising that reaches the right audiences in real time, cutting wasted spend and lowering the cost per sale. Chatbots can handle customer queries 24/7 at low cost, improving service, and predictive analytics can forecast demand to inform stock and promotion decisions. For a retailer competing on convenience and price, these efficiency and targeting gains could be a significant competitive advantage, and rivals adopting AI may make it increasingly necessary just to keep pace.

However, heavy investment carries real risks. AI systems and the skills to run them are expensive, and the payback is uncertain for a retailer that may lack scale. Using large amounts of customer data raises data-privacy and regulatory concerns — mishandling data could bring legal penalties and damage the brand's reputation. There is also a danger of over-reliance on algorithms that may be biased or simply wrong, and of losing the human touch that some customers value. AI is also changing rapidly, so today's expensive system may quickly be superseded.

Judgement (criterion). Whether the retailer should invest heavily depends on its scale and data, the cost relative to expected returns, and how well it can govern the data. For a large retailer with rich customer data and the resources to manage privacy and oversight, heavy AI investment is likely worthwhile, because the targeting, personalisation and cost savings scale up and the firm can absorb the cost. For a smaller retailer with limited data and budget, a more cautious, phased adoption is wiser — adopting proven, lower-cost AI tools first rather than investing heavily upfront. On balance, the retailer should invest in AI, but the scale and pace of investment should match its size, data and ability to manage privacy and oversight — heavy investment is justified mainly where the firm can realistically generate and govern the data AI needs.

IT and AI have reshaped marketing over the last two decades, from e-commerce and social media to AI-driven personalisation and targeted advertising. Whether this change has been 'for the better' depends on whose perspective is taken and how the technology is used.

The case that technology has changed marketing for the better. First, technology has made marketing far more targeted and measurable. IT data analytics and AI programmatic advertising reach precisely the customers most likely to respond, reducing wasted spend, while digital tools allow results to be tracked and improved in real time — a sharp contrast to costly, untargeted mass advertising. Second, it has enabled personalisation: AI recommendations tailor offers to each customer, raising relevance, conversion and sales. Third, technology has lowered cost and widened reach: even small businesses can sell globally through e-commerce and market cheaply through social media, levelling the playing field with larger rivals. Fourth, chatbots and automation provide faster, round-the-clock customer service at low cost. For businesses, these are major efficiency and effectiveness gains, and for many customers more relevant, convenient marketing is genuinely better.

The case that the change is not wholly for the better. First, the heavy use of customer data raises serious privacy concerns and regulatory risk, and some customers find personalised, tracked advertising intrusive. Second, there is a risk of over-reliance on algorithms that can be biased, manipulative or simply wrong, and AI-generated content can be generic or misleading. Third, technology has costs and a skills barrier — systems are expensive and change rapidly, so businesses must keep reinvesting, and those that cannot may fall behind. Fourth, automation can erode the human relationship that builds genuine loyalty. So the change brings new problems alongside its benefits.

Weighing it up (criterion). Whether technology has changed marketing for the better depends on how responsibly it is used and from whose viewpoint it is judged. Used with strong data governance, human oversight and genuine customer benefit, IT and AI clearly improve marketing — more relevant, efficient and far-reaching. Used to exploit data, manipulate customers or replace judgement entirely, the change is more questionable, and customers may experience it as worse.

Judgement. On balance, technology has changed marketing largely for the better for businesses — it has made marketing more targeted, measurable, personalised and cost-effective, and has opened global markets even to small firms. But it is not unambiguously better: the gains depend on responsible use, and the privacy, over-reliance and cost concerns are real. The most defensible conclusion is that IT and AI have improved marketing significantly where they are governed well and aligned with customer interests, but the technology is a powerful tool whose benefit is conditional — it has changed marketing for the better mainly when businesses use it ethically and keep human judgement in the loop, rather than automatically in every case.