M&S Just Set a Target Most Stores Are Ignoring

According to Reuters, Marks & Spencer has set an ambitious goal - lifting its online share of Fashion, Home & Beauty revenue from around 34% to 50%, backed by £120 million in automation and technology investment over three years. That is not a tweak. That is a strategic declaration.

For large retailers, big targets attract big headlines. But the more interesting question is what the rest of the ecommerce world should take from it. Because buried inside that ambition is a set of problems that every online store owner - large or small - is quietly dealing with right now.

The Real Problem Is Not Traffic. It Has Never Been Traffic.

For too long, ecommerce teams have treated traffic as the primary metric and pointed fingers when conversions fall short. Marketing drives visitors in, the digital team takes the blame when they leave, and the cycle repeats. This is a fundamentally broken model.

Growth does not live in any single team or channel. It lives in the unglamorous work - returns processes, fulfilment reliability, product information quality, the moment a shopper lands on a page and cannot find what they actually need. These are not back-office problems. They are conversion problems.

M&S's £120m investment signals they understand this. Technology investment at that scale is not just about building a prettier website. It is about rearchitecting how shoppers are served across every touchpoint - and making sure every part of the organisation is pulling in the same direction.

If you want to understand why so many ecommerce stores are haemorrhaging revenue despite solid traffic numbers, this piece on why traffic is not your problem is worth reading first.

What Most Stores Get Wrong About Their Own Shoppers

Customer Intent Is Not the Same as Search Behaviour

There is a significant difference between a shopper who types "blue linen trousers" into a search bar and one who is thinking "I need something cool and relaxed for a summer wedding." The first is a search query. The second is an intent. Most ecommerce stores are only equipped to handle the first.

Understanding intent requires understanding context - who the shopper is, what problem they are trying to solve, what stage of the decision they are at. This is precisely where most ecommerce experiences fall apart. Product pages are built to describe, not to guide. Search bars return results, not recommendations. And customers leave without buying.

As we have argued before, shoppers think in needs, not categories - and stores that fail to meet them there pay a heavy conversion price.

Generational Behaviour Is Splitting Your Shopper Base

Here is a reality that many ecommerce teams underestimate: different generations shop in fundamentally different ways. Older shoppers tend to be search-native - they know what they want, they type it in, they expect clear results. Younger shoppers, particularly Gen Z, are more socially native - they discover products through content, through recommendations, through inspiration rather than intention.

This means a single discovery model no longer works. If your store is built purely around search and category navigation, you are already invisible to a growing portion of your potential customers. The stores that win will be those that serve both behaviours simultaneously - meeting the purposeful buyer with precision and the inspired browser with relevance.

Are Your Customers Trained to Wait for Discounts?

This is an uncomfortable question, but an important one. If your primary mechanism for driving conversions has been promotional offers and discount codes, you may have inadvertently trained your shoppers to wait. They have learned that patience is rewarded. That full-price is negotiable. That the right time to buy is always slightly later.

This creates a dangerous dependency. Margins erode. Brand value softens. And the moment you try to step back from discounting, conversion rates drop sharply - not because your products got worse, but because shopper behaviour was shaped around an expectation you created.

The better path is to build enough value into the shopping experience itself - through guidance, personalisation, and genuine confidence in the purchase - that discounts become unnecessary rather than inevitable.

The Attribution Blind Spot Nobody Talks About

Who in your organisation actually owns customer data? And what is being done with it? These two questions expose a gap that exists in most ecommerce businesses.

Multi-touch attribution - understanding which interactions across which channels actually drove a purchase - remains poorly understood and poorly implemented outside of enterprise-level teams. Most stores are still looking at last-click data and drawing conclusions that are, at best, incomplete and, at worst, actively misleading.

The result is budget being allocated to channels that look productive but are not driving incremental growth - while the real friction points (the product page that confuses, the question that goes unanswered, the shopper who nearly converted but did not) stay invisible.

If your analytics are telling you a clean story about why shoppers convert or leave, they are probably lying. Here is what they are likely missing.

Building for Growth Means Building Around Expertise

One of the quieter lessons from retailers doing this well is the importance of building teams around genuine specialists rather than generalists who cover everything at a shallow level. M&S's willingness to invest significantly in technology reflects a broader commitment to treating ecommerce as a discipline that deserves serious, expert attention - not just a channel to be managed alongside everything else.

For smaller stores, this does not necessarily mean hiring a dozen specialists. But it does mean being honest about where expertise gaps exist - and filling them with tools and technology that can compensate for what a lean team cannot do alone.

Where AI Changes the Equation

This is where ecommerce conversion optimization AI stops being a nice-to-have and becomes a genuine strategic lever. The shopper intent problem, the generational behaviour split, the product discovery challenge, the need for personalized shopping experience AI at scale - these are not problems that can be solved by adding another team member or running another campaign.

An AI shopping assistant for ecommerce can do something that no static product page or search bar can: it can engage with a shopper in the moment, understand what they are actually trying to achieve, and guide them towards a confident purchase. It can serve the search-native shopper with precision. It can serve the inspiration-driven browser with relevance. It can make AI product recommendations that feel genuinely helpful rather than algorithmically random.

And critically - it generates data. Real behavioural data about what shoppers are asking, what they are confused by, what is stopping them from buying. The kind of signal that turns attribution guesswork into genuine intelligence.

This is what conversational commerce AI looks like when it is built to serve the shopper first - and the business benefits follow directly from that.

M&S is investing £120 million to get there at scale. The question for every store owner is: what version of that shift is available to you right now?

This article was inspired by Reuters.

LISA is an AI shopping assistant built to close the gap between traffic and conversion - by guiding shoppers, understanding intent, and surfacing the insights your team needs to grow. If you want to see what that looks like on your ecommerce store, book a demo and we will show you exactly how LISA works in practice.