Winning online retails zero-sum game

The winning strategy

Winning at online merchandisings zero-sum game requires a radically different approach an approach with intelligent automation at its heart.

Indeed, it is an approach that is impossible without automation, because the sheer scope of merchandising executions and agility it requires cannot be delivered the old fashioned way.

The key question here is not how to win the game, but how many games to play. That is, do you play one game, look for the ideal mix of products for an imaginary average shopper, or play several, segmenting according to arbitrary typologies and personalise some areas of the real-estate

Or do you do neither of these Do you instead play almost as many zero-sum games as there are visitors to your site

The truth is, in a fast-moving world of fragmented shopper behaviour, the right approach is to treat every shopper as their own segment – and serve each with their own highly relevant experience. The result is a merchandising operation that redefines the zero-sum game to optimise both product exposure and product relevance for every shopper.

” Relevance: Personalising the retail experience down to ever more granular segments maximises the opportunity to deliver relevant experiences and merchandise relevant products
” Exposure: Product exposure is maximised too, because individually personalised merchandising is no longer limited by screen real estate, but by the number of eyeballs looking at it.

Intelligent automation

Clearly, however, this kind of approach presents some fairly significant challenges for ecommerce merchandising departments still labouring to maximise product exposure the old way largely manually and with tools that do not support the flexibility, agility and scale required to deliver truly personalised experiences for every shopper.

Not just that, but experiences that learn from and adapt to crowd and individual behaviour in real time.

There is only one way to deliver merchandising on that scale one to one, whole site personalisation and that is through automation.

Not just automating basic workflow tasks, but automating entire merchandising operations, with human input focused where it should be on high level, strategic issues rather than the day to day of which products are displayed where.

Today, very few retailers have this capability. Those that do have adopted a new generation of merchandising automation tools, which draw on big data, cutting edge machine learning and predictive analysis to deliver true personalisation. This means maximum product exposure, maximum individual relevance and the ability to adjust everything in real time. It is only a matter of time before the rest of the market catches on.

Case study: How Ginza is winning the zero-sum game

Music, films, games and books retailer, Ginza, has automated product assortments across search, recommendations and merchandising but uses a solution that also, crucially, enables high level merchandising input, by providing the ability to fine-tune recommendation data.

It has achieved stunning results – reversing a four-year downward trend to realise a 20 per centincrease in total sales, 12 per cent higher conversion rate and four per cent average order value uplift.

Johan Svenstrand, ecommerce and technical manager at Ginza, explained: For the first time since 2009 we’ve increased revenues, transactions, cart value and conversion rate. With eSales, we see an ROI of at least three times when taking into account increased costs for licenses and hardware.

Michael Mokhberi is the CEO at Apptus

John Lewis is currently hunting retail innovators for 200,000 JLAB 2016 accelerator

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