How Today’s Buyers Want To Buy

The processes surrounding shopping, buying and selling are constantly evolving to accommodate changing social norms and customer preferences. A retailer that fails to appreciate this risks alienating its customer base. Moreover, in a world where customer loyalty is so easily lost to competitors, the issue of how shoppers want to buy is something that retailers need to address to sell products and services successfully.

 

This article will address how today’s buyers want to buy from the point of view of e-commerce sales and sales from bricks and mortar shops.

 

 

 

There is a persistent debate surrounding which sales platforms are growing the fastest and which are most popular with customers.

 

Most analysts agree that online sales are now more prevalent than sales from bricks and mortar stores. This is a global trend, not one confined to the UK. Research commissioned by PWC and conducted by the Local Data Company suggested that an average of 11 stores opened on the British high street in 2017, which was down from 12 stores per day in 2016 and 15 per day in 2013, reflecting the surge of online retail spend compared to spend in physical stores. Difficult economic conditions have been exacerbated by the fact that in 2017, UK wages failed to keep up with inflation and this resulted in less consumer spending. Additionally, consumers continue to flock to online shopping as opposed to shopping in physical stores due to the convenience offered and the different delivery options available.

 

The research also looked at store closures and found that an average of 16 high street stores were closed every day in 2017, again reflecting the growth of online sales at the expense of physical stores. The reasons underlying the shifts towards online buying as opposed to bricks and mortar stores sales are complex and continue to attract debate. Surprisingly the research found that although there is an overall depreciation in growth of bricks and mortar stores, compared to online merchants, this trend was not uniform across all sectors. The trends towards slower growth in high street stores, were found not to apply to beauty product shops, speciality coffee shops, ice cream outlets, tobacco shops and book shops. All of these sectors grew despite tough economic conditions for the management of physical stores including high rents, burdensome rates bills, growing utility bills, staff costs and losses associated with theft and spoiled stock. The growth in these areas has been attributed to the emergence of new consumer segments and so called “experience-seeking” millennials.

 

There has also been a growth of so-called “clicks and mortar” stores. One example is the Amazon pop-up shop initiative, which proposes to open pop-up shops across the UK in 2019. These unique popup shops aim to provide high tech physical shop outlets for successful online brands. The emergence of popup shops supports the contention that although the high street is in overall decline, the decline is not uniform. Rather, there is a decline in stores that fail to provide a good experience to shoppers by offering them an experience that can’t be replicated online. This further suggests that focusing on improving customer experience of a physical store can reverse or reduce the current problems experienced by high street stores.  

 

 

 

Retailers have been quick to realise that AI can be used to improve the experiences of shoppers in high street stores. For example, AI has been employed in the form of large, interactive screens which allow customers to check information like store layout, opening times and returns processes. This is in many ways a mutually beneficial approach, because the customer can access information they need, quickly and conveniently, whereas the retailer’s human shop assistants have more time to focus on tasks that are less repetitive.

 

Some retailers have gone as far as to create in-store robots capable of interacting with customers. Sometimes dubbed as “new age salesmen”, the in-store robot can approach shoppers as they enter the shop, and enquire if they can be of help. These robots offer an even more personalised service to the customer, because the customer does not need to look for static interactive systems like large screens. Customers can use the robot to get helpful directions about where certain products are located. Customers can use large screens located on the robot, or verbally tell a robot what their queries are. Robots are often equipped with voice technology, resulting in a “human-like” experience for the shopper. Some retailers have designed robots to guide customers to the aisle where the products they require are located, and other retailers have even designed robots that can physically assist in carrying heavy purchases to the checkout or to the car park. Other robots are capable of making recommendations about additional purchases, in effect upselling and cross selling to the customer. This level of personal service makes customers’ in-store experience much easier and increases the likelihood that customers will return. Research has shown that one of the main reasons customers buy online is to save time. As such, when a physical store introduces measures that help a customer to save time, this reduces the temptation to buy online as opposed to in a physical store.

 

Out of stock status is hugely problematic for modern customers who seek convenience from their in-store experiences. There is nothing more disappointing for a customer, than taking time out of a busy schedule to go to a shop, only to find that items, usually in stock are now out of stock. Retailers have been using AI to address this problem. Robots fitted with cameras scan stock levels to monitor stock levels. Then robots can be used to physically carry out the restocking process. This reduces the incidence of “out of stock” status, reducing losses due to lost sales, and also reducing potential for damage to a brand reputation that can arise where a customer has entered a shop only to be disappointed to learn that items are out of stock.

 

AI is also being used proactively to pre-empt problems that might be experienced by customers. An example is AI used by Walgreens which has been adapted to monitor flu-levels in certain geographical areas. Flu levels are then used to predict how many flu related products may be needed at a given time, and restocking is then performed according to this analysis. This helps the store avoid out of stock status that may arise unexpectedly as a result of surges in demand for certain products.

 

 

 

AI has been used to solve common problems experienced by customers, with the aim of attracting higher levels of footfall. One example relates to finding the correct shade of makeup in-store.

 

Traditionally customers buying makeup from a store would have to painstakingly select the correct shade, always running the risk that they would make an error. While in-store many makeup customers would elect to test some product on the inside of their wrist, or get an experienced sales advisor to apply makeup and select the correct shade, based on their expertise.

 

There are many problems with this approach. Testing using small amounts of makeup can be time-consuming and messy. Many people complain that the skin on other parts of the body doesn’t exactly match the skin on the face, and applying even small amounts of makeup to the face, for a more accurate measure, requires the customer to either wash their face afterwards, or continue to wear the makeup until they have time to remove it. Additionally, customers may wish to try several shades, but every time this is done, the previous sample would have to be removed, or else the customer is left with uneven shades of makeup. This is all time consuming, inconvenient and can stain clothes. It also alienates customers shopping at certain times, for example people on their lunch break may avoid sampling makeup because they need to return to work and may not wish to do so wearing makeup, or having changed their existing makeup.

 

Sephora, a makeup retailer has designed a complete system to quickly and easily identify the correct shade of makeup, without any need to apply different products to the skin. Their application, Colour IQ scans the customer’s face, and then matches their skin colour to different makeup products. Related products have addressed the same problem as it relates to lipsticks. Lip IQ scans a customer’s face and lips and then matches the correct shades that compliment the customer’s skin tone.

 

The retailer North Face has devised similar technology to solve the problem of finding the perfect coat for a person’s shape. Traditionally, finding the perfect coat could be a time consuming endeavour. Different coats exist to suit many different pursuits and activities. Specialist material may be needed depending on different weather conditions and different features may be sought after for different types of activities like mountain climbing or hiking. Due to the size of the North Face range, customers browsing coats may become frustrated or overwhelmed by the choice available. The technology devised by North Face addresses all of these problems. It is a wholly automated system which asks the customer a lot of questions about the type of coat they want, the type of activity and weather they will be wearing the coat during and what features are important to them. The results are then analysed and a set of personal recommendations are made to direct the customer to the best coat for them.

 

Also of pivotal importance is the in-store experience of the customer with factors like lighting, store layout and density of merchandise on display all influencing the customer experience.

 

 

 

The first few months of 2019 saw heavyweight brand Debenhams exhibit signs of financial stress. When it later transpired that the brand was close to bankruptcy and there was a hurried buyout of the brand by Sports Direct owner Mike Ashley, people started to ask what went wrong. One of the factors shown to be in dire need of change was how the brand’s shop floors were organised, and this was something that was addressed when the Debenhams new management team took over. Shop floors were overly full and items were crammed on display in a poorly organised fashion. Research into how today’s buyers want to shop shows factors like this as highly likely to alienate the millions of shoppers who visit Debenhams stores in a daily basis. The irony was that the entire Debenhams estate was attracting high rent and rates costs, when some stores were not attracting enough footfall to justify their continued operation. One of the first things the new Debenhams management team did was to announce the closure of 50 stores from the start of 2020.

 

Also high on the list of the new Debenhams management’s list of priorities was the creation of novel in-store experiences that could not be replicated online. Some stores announced plans to open gyms as part of their in-store facilities and others focused on improving concierge services within the stores themselves. Even more novel concepts like “beauty bars” have also been introduced in some Debenhams stores, to allow customers to combine a shopping experience with getting a range of beauty treatments including blow drys.

 

The new management team also set about improving the click and collect facilities in the store, in recognition of how popular omni-channel shopping has become.

 

Despite the brand’s overall financial stress, Debenhams digital sales rose by almost 10% in 2018. However, experts deemed the overall digital strategy to be in need to cope with the growing demand for products sold online.

 

Successful Debenhams stores were used to decipher what customers want and what drives footfall. One example used was the Debenhams store opened in Stevenage in 2016. Despite having 20% less product density on its shop floor, compared to other Debenhams stores, its sales and profits were much higher than average.

 

Wider lessons can be learned from the transition that has buoyed Debenhams through uncertain financial times, and inferences can be drawn about how customer preferences and norms have changed.  One such norm is delivery options and the rise of omni-channel and multi-channel sales.

 

 

 

Omni-channel and multi-channel sales involve selling products and services through different channels including physical and digital sales channels. These methods of selling have evolved in direct response to customer preferences for convenient ways to buy, collect and return items bought. It signals a move away from the traditional mode of shopping which involved a high degree of “separation” between in-store sales and digital sales. Some experts have referred to this as the use of “silos”, where returns and sales were not coordinated across sales platforms, so for example someone could buy an item digitally but were not allowed to return that item in-store. Stores that have addressed this demand for more convenience have performed much better compared to their more traditional counterparts, as research on customer preferences continues to highlight how customers want a more “seamless” interaction with brands across their various sales platforms.

 

 

 

Ecommerce giant Amazon has used data advantageously to create a more personalised way for consumers to make their purchases. Data gathered and analysed allows for a series of “suggestions” as to what to buy to be made and shown to the online consumer as they progress their online purchase. Many will stop to consider the suggestions and some will purchase additional items based on the suggestions. Amazon and other online retailers putting this strategy into practice are further augmenting their sales through offering discounts for additional purchasing and in particular for bulk purchasing. So, for example some retailers will offer an overall discount, or incentives like free delivery if the customer spends over a certain price threshold.

 

The suggested purchases are based on unique data derived from external and internal sources. Internally sourced data may be based on a consumer’s past spending habits, for example, Asda analyses the past orders of their customers and these are displayed alongside the shopping basket when customers place subsequent orders.

 

Amazon uses both external and internal data to make cross selling and upselling suggestions to their customers. So for example, some customers will see “frequently bought together” suggestions appear beside their shopping baskets as they progress an order. This will reflect the items that are most commonly purchased together from the Amazon platform.

 

 

 

Convenience and speed are two of the most important factors that shape modern customers’ buying preferences. Shoppers don’t want to spend too much time browsing through over-stocked or confusing shop layouts, they want to find their products quickly and easily. In many ways these preferences have driven the tendency towards buying online as opposed to in physical stores. However, leading retailers have adapted to these preferences and in doing so have been able to encourage more shoppers back to shop in physical stores. The main adaptions include the use of AI to make shopping more convenient and less time consuming for the customer. Less dense shop displays have also been very helpful, as shoppers are able to find what they want much more quickly. More convenient delivery options and a “seamless” retail experience across digital and physical sales platforms have also been instrumental in encouraging higher levels of footfall, because shoppers want a range of choice in terms of delivery options.