On-line merchandising

Mid-sized retailers are often in a difficult position. The majority do not have the luxury of a large marketing budget. However, rather than throwing cash at trying to entice their customers to spend their money via unfocused special offers they could look to use technology that will help them deliver results for the business. Also, online shopping continues to buck the downward trend seen on the UK High Street. Latest figures show the ‘e-tail’ sector made a 13% annual sales increase in February, so there’s never been a better time for retailers to engage with their customers in this sector.

Selling clever

One option is to look to implement Maginus’ e-Commerce solution with its ‘Clever Selling’ module which utilises Microsoft SQL Server 2008 Business Intelligence Architecture and Data Mining functionality. The module has real time connectivity to Microsoft Analysis Services 2008, allowing retailers to introduce a very sophisticated on-line merchandising approach to medium sized organisations.

The ‘Clever Selling’ module is, in effect, an artificial intelligence capability which allows companies to influence a customers buying decision by very cleverly merchandise their products on the web. It enables the web site to learn about its customers and its products. The more business that goes through the web site, the more it learns. The web site not only learns about products the customer has bought, but it can also analyse what other products the customer has looked at, what search criteria they have used and can therefore offer complementary products based on all these elements.

This tool is all about delivering a complete merchandising experience which doesn’t impose itself on the customer’s overall shopping experience. When a customer views products or places an order it allows the website to suggest other products that the customer might be interested in purchasing. By both cross-selling and up-selling the overall aim is to maximise the AOV (average order value) of each customer transaction.
 

By building up knowledge at both customer level and at product level, it allows the retailer to build up an idea of an individual customer’s shopping habits and the habits of all customers. For example, on a basic level, the web site will know that a high percentage of customers who bought ‘Product A’ (e.g. black trousers) also bought ‘Products B’ and ‘Product C’ (e.g. red jumper and white shirt). The site will therefore merchandise the red jumper and white shirt to shoppers who buy the black trousers.

However, such technology can be much more sophisticated than that and can delve much deeper to reveal even greater customer insight as and when required. For example, it can recognise that a particular customer is within a particular customer type. That type can be based on a whole list of topics, such as age, geography, whether they only buy promoted products, never spend more than £50 on the web etc.

So, at a more sophisticated level the site can recognise that ‘Customer 1’ is based in the South East of England & that customers in this region prefer a green jumper rather than the red one and the site will therefore merchandise the green jumper.

As well as recognising different customer types and the different products that customers buy, it can also recognise that different products are bought at different times of the day. It can also assess how shopping habits in the week may be different from weekend shopping habits. So, for a customer buying the pair of black trousers at the weekend the site will merchandise different products (possibly more casual clothing) at the weekend.

For the retailer, the web site becomes the ‘perfect shop assistant’ able to judge for each customer what they would most prefer and therefore are most likely to buy. It is also able to assess how the customer likes to be contacted with offers and how regularly. The aim is always to improve the customer experience of the site, so customers feel that businesses are communicating with them directly and giving them offers that they will be interested in. However, for a business this should also help to sell more products and boost profits.

This type of technology provides flexibility so each company can choose which criteria they want to use to measure customers. For example, what customer types they want to consider; such as age ranges, geographical location etc. If all this information is stored on file, trends and patterns can be monitored closely.

Pushing ahead

E-tailers should also be using e-marketing campaigns to help drive sales. Companies should be getting at least 60 per cent of their business from email offers, also known as ‘push emails’. Employing clever selling techniques in this area could help ensure that emails to customers are targeted. For example, by adopting such techniques retailers could identify when customers have visited the site, which pages or products they’ve looked at and even if they’ve put some products in a basket which they have then abandoned. Based upon this information retailers can also send targeted email offers which should then achieve a much higher conversion rate, rather than just a standard blanket offer sent via email.

The introduction of sophisticated merchandising capabilities at an affordable price can help provide mid-sized retailers with essential data that will help them gain valuable information in their quest for success.