How is Artificial Intelligence Shaking Up Retail?


Buying experiences, both in offline and online channels, can vary dramatically between retailers as vendors approach the customer journey in different ways. However, Artificial Intelligence (AI) is increasingly becoming a common tool used across the retail sector; being incorporated both into online and offline channels with the goal of enhancing customer experience or increasing conversion rates. AI is starting to shake up the retail sector and there are many different ways in which AI could be used in the future to increase retailers’ revenues and improve customer experience levels. As AI increases its presence, the question that lies ahead is: will it change how retail operates forever?

 

Enhancing the in-store experience through AI

Smartphones are our constant companion when we shop; it doesn’t matter if we are browsing the aisles or trying something on, they are always there with us. So, how can retailers take advantage of this to enhance customers’ experience when in store?

Providing information in-store through smartphones could be the starting point. Having the possibility of checking if there’s more stock of a certain product, using QR codes could speed up the buying process. Less time spent by the shopping assistant checking stock availability means more time dedicated to advising the customer on other products to buy, enhancing the customer experience and increasing the rate of sale.

Another opportunity arises for retailers to use AI to improve the in-store experience by giving the customer the possibility of checking how the whole range of other colours or sizes for each product would look on them. This is already being done by Rebecca Minkoff in New York; providing customers with smart dressing rooms that allow shoppers to interact with a display screen (1), enabling them to carry out other activities from their smartphone like adjusting the lighting of the dressing room itself or even asking for assistance without having to go outside. 

 

Increasing conversions online with AI

Improving the in-store experience is a powerful way to foster customer loyalty, so what can retailers do to replicate this experience online? Using AI powered virtual assistants like chat bots could help to replicate this personalised experience by guiding the customer through the purchase process and answering any questions along the way.

Luxury or complex goods in particular highly benefit from the use of AI in the online buying process as customers tend to have a number of questions when buying these types of products as they want to be reassured they are making the right choice. On many occasions, in the modern high-street shop, shopping assistants often lack specialised product knowledge to be able to help the customer effectively. If these types of stores want to up their game online, having an AI chatbot that can advise the customer on particular product information, whilst answering any concerns, cannot only enhance the shopper’s experience but also increase conversion rates.

By learning how people buy and make appropriate product recommendations after analysing how they interact with your site and engage with your products, AI tools can increase the average value spent by each customer by choosing carefully selected product recommendations.

 

Staying in touch

Social media can also become a data hub for AI platforms. By engaging with customers’ social media, AI technology can learn what customers like and dislike, their buying habits, and other types of information that can be used to make personalised recommendations about specific products; these recommendations can be made at the right time, and, at the right place. In other words, data mining can help retailers to get to know their customers much better and as a consequence, provide them with an improved and more personalised service. AI tools enable retailers to learn about individuals en masse and make well-timed offers that are personalised based on what information they share online.

By learning in depth about customer behaviour, AI technology can help by developing reminders to buy, following abandoned carts, discovering the optimum time to position a product, or knowing when to drop or raise priced.  This enables retailers to stay in touch with customers without constantly bombarding them with information, and thanks to the use of AI and data mining tools, retailers can engage with their customers at the right time and place, without the risk of overwhelming them.

 

AI technology can bring personalised marketing one step further by helping retailers to get to know customers in depth and learning about their individual buying habits, offering products to customers at the best time. Using AI, retailers can optimise both online and offline channels, enhancing customers’ experience at the same time as improving conversion rates.  

 

 

References

(1)   http://multichannelmerchant.com/ecommerce/how-retailers-are-redefining-the-shopping-experience-26012017/

(2)   https://www.entrepreneur.com/article/288098

(3)   http://www.adweek.com/digital/5-bleeding-edge-brands-are-infusing-retail-artificial-intelligence-175312/

 

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Are shipping fees costing you customers?


Online retailers are becoming incredibly sophisticated in their ability to impact conversion rates based on hundreds of different variables.  New technologies allow purchase screens to be customised based on user data (i.e. where a customer is purchasing from, time on site, location, previously browsed items, etc.), meaning that e-retailers can be super-specific in their targeting to increase conversion rates.

There’s already been a lot of work done to get the customer to the stage where they are ready to click “add to basket”.  You’ve attracted customers to your site, you’ve encouraged them to commit to a product, and it’s in their virtual basket – ready to be purchased.

So what is stopping customers from moving forward to complete the purchase?

It’s all comes down to one decision – to buy or not to buy.  Psychology sits at the heart of these decisions, and understanding the reason for your abandoned cart rates can help you to break down the psychological barriers that are stopping customers moving forward.

In a report from UPS (1), the number one reason for abandoned carts globally was that shipping costs increased the overall purchase price more than expected.  If we think of the psychological process at play here: customers firstly decide on a product, are happy with its price and features, and then add the product to their basket.  The customer is mentally calculating the overall price as they add their different products to their basket.  They then move forward to complete their order and shipping costs get added.  The price is more than they were expecting.  Or maybe it is the price they expected but mentally it pushes the overall basket value above their desired purchase price.

And this is enough for customers to abandon their cart and leave their items.

One factor that could be causing the high rates of abandoned carts is that customers are testing the shipping costs and how they will be applied to their purchase.  The cart isn’t really abandoned as it was never a serious purchase – it was an elaborate calculator to help gauge the shipping costs.

Even removing this factor, the addition of shipping costs is still a blocker for customers moving ahead with their purchase.  So what can you do to step in and impact the psychology of your buyer so the sale has a higher chance of moving ahead successfully?

How could retailers address this issue earlier?

Is it an option to try and put this (even potentially unattractive) shipping time and cost information upfront so that customers are not surprised when they see shipping costs added?  Could shipping costs be added to the basket subtotal when any items are added so that the increased cost is not a late addition to the purchase journey?

The other option is offering free shipping and removing shipping costs altogether.  This could put a strain on profit margins, but it might be a worthwhile activity to calculate the cost of abandoned carts to the business – i.e. what revenues would you have achieved if even just 10% of those customers had completed their purchased, and compare that to the cost of offering free shipping costs to all customers.

If this is a psychological blocker that is preventing customers from buying then increasing product costs ever so slightly across the board to account for the cost of offering free shipping could mean the difference between high rates of abandoned carts and winning lots of new customers.

In Europe, 49% of consumers abandoned their cart because shipping costs were too high (which is actually lower than the averages in the US at 54% and Canada at 61%).  Sometimes this is because orders haven’t been large enough to result in free shipping qualification.

Is the problem actually worse than we expect?

However, some reports show even higher numbers of abandoned carts due to shipping costs.  A report highlighted in eMarketer by FuturePay (2) said that 86% of surveyed respondents said that the cost of shipping resulted in cart abandonment, so it’s clearly a problem for retailers to get right.

Amazon has tried to combat this barrier through its free shipping option for Prime members.  A report by cg42 (3) found that 91% of Amazon Prime members said they signed up for the service based on the lure of free 2-day shipping.

What appears to be a common theme amongst many of the reports and surveys is that better communication about delivery options and associated costs in order set more realistic expectations earlier on.

A report by Meta Pack (4) found that 66% of consumers would move to another brand if there were more attractive delivery options available – so shipping costs and delivery times are clearly at the top of the agenda for customers when deciding whether to complete a purchase.

An article from the Royal Mail highlights an interesting point from a Deloitte report (5), saying that retailers will need to move quickly to better respond to consumers’ expectations – with same day delivery becoming more standard in our online shopping experiences. Some reports are also suggesting many consumers are now expecting free same-day delivery which is obviously ahead of many retailers’ current offerings.

So with the huge impact that delivery costs have on customers’ decision making, combined with the availability of suitable delivery options, retailers will be looking at ways to make delivery option information more prominent on their sites to enable consumers to be updated earlier in the process.

 

References

(1)   https://www.ups.com/media/en/gb/ups_global_paper.pdf

(2)   https://www.emarketer.com/Article/Cart-Abandonment-Really-Come-Down-Cost/1015092

(3)   http://cg42.com/

(4)   http://www.metapack.com/report/delivering-consumer-choice-infographic/

(5)   http://www2.deloitte.com/au/en/pages/consumer-business/articles/global-powers-of-retailing.html

Are you in the 20% of retailers with no mobile offering?


In the UK, mobile now accounts for 40% of online retail sales – and that’s a figure which is growing rapidly quarter on quarter (1). Yet for many consumers, the mobile shopping experience is still poor, and retailers are not delivering a good enough experience to convert would-be customers.

Many customers are struggling to make it beyond their shopping carts when buying on a mobile device – with desktop sales conversions 2.7 times higher than mobile conversions.  In other words, only 19% of shopping carts accessed using a smartphone result in completed purchases versus 30% on a desktop (2).

There is clearly a disconnect, as it’s reported that shoppers in the US spend 59% of their ‘web’ time on a mobile device, but only 15% of their dollars are being spent through mobile channels (3). Bad connectivity can sometimes prevent sales moving forward if shoppers are accessing sites on the go, but it appears to be often down to poor mobile experiences offered by retailers.  Many merchants have not got dedicated apps, or at the very least haven’t yet optimised their sites for mobile.  Shoppers are having to go through the standard desktop purchase process using a tiny screen – and it’s often not possible to easily complete the purchase from a mobile device because of non-optimised payment gateways.

Is mobile stopping your customers finding you?

And this lack of optimisation is not just affecting shoppers trying to complete purchases – it’s impacting customers even finding you in the first place if you don’t have a mobile optimised site.  Google is now prioritising the ranking of websites that are optimised for mobile above non-optimised retailers – so ignoring smartphone and tablet traffic will start to cost ecommerce outlets from both sides (3).

In fact, research by the Centre for Retail Research has said that the UK retail industry is losing £6.6bn every year due to a lack of investment in a mobile optimised solution (1). Retailers need to look at ways to improve each part of the sales process – from viewing products, obtaining stock information through to actually placing an order.  It’s not just about having a mobile optimised site – it’s about being able to access easy to use payment methods that are also optimised for engagement on a mobile device.

1 in 5 retailers have no mobile offering

The same research also suggests that 1/5 of retailers in the UK still have no mobile offering – despite 88% of retailers saying that having a mobile channel would result in more visits to their store.  This is because many consumers are not exclusively using online and in-store channels to purchase, but are instead using a blend of the two by consulting their smartphones in store before making purchases in-person.

So there is clearly demand from consumers, and retailers can see the potential benefits that could be realised from having a dedicated mobile strategy, yet 40% of UK consumers still feel the mobile experience could be improved so there is more work to be done (1). When the top searched for items are high-value products such as clothing and electronics, it surely makes sense for retailers to try and address the huge gap between abandoned carts on mobile devices and abandoned purchases on desktops.  If the only difference is that the device used by consumers is mobile, then the reasons must be down to poor experience due to lack of optimisation for smartphones.  It’s a factor that is within the retailer’s control – so how can they take advantage of this opportunity?

Moving to mobile optimisation

We all know that websites need to be mobile optimised, but for retailers it’s a matter of commercial life and death.  Without optimisation, customers can’t easily purchase or navigate through payment screens.  If the text is too small to read or consumers have to zoom in to input their bank details then it’s unlikely that they will be keen to return to complete their purchase, nevermind return in the future.

The easiest step is to build mobile responsiveness into any new web design projects from the ground up.  You don’t need a dedicated app – you just need a mobile optimised website that can organise your information into a mobile friendly view.  Regular testing is critical to ensuring information is displayed how you intend it – but many of the main ecommerce software tools offer mobile responsive functionality to help you easily offer mobile purchasing options.

Another option is to focus on the visuals.  Keep your mobile offering clean and full of visuals so customers are not having to zoom in on small text or having to click small buttons within chunks of information.  Make it visual and easy to navigate from a small screen.

Less text is more on a mobile device, so let the pictures do the talking (4).

Many forward-thinking retailers are moving to a mobile-first design – they design their ecommerce stores with mobile at the forefront of what they are doing, and desktop comes second.  As more consumers are purchasing off their mobile, why design for a declining market?

So, are you on board with mobile?

 

 

References

(1)   https://econsultancy.com/blog/66543-50-fascinating-stats-about-mobile-commerce-in-the-uk-2015/

(2)   http://www.cmo.com/adobe-digital-insights/articles/2016/10/19/adi-holiday-predictions-report-2016.html#gs.E58tvoI

(3)   http://uk.businessinsider.com/mobile-commerce-shopping-trends-stats-2016-10?r=US&IR=T

(4)   https://www.shopify.com/partners/blog/74754051-5-simple-hacks-for-an-optimized-mobile-ecommerce-design

 

Infographic guide to Predictive Analytics in Retail


C24 has just published an infographic / visual version of our Predictive Analytics in Retail whitepaper here that just includes the main key points from our overall whitepaper (which can be accessed here).

c24-predictiveanalyticsinretailpic

C24 is pleased to announce their latest whitepaper on Predictive Analytics in Retail.

 

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

Smart Stores of the Future: Predictive Analytics


How is the Internet of Things poised to revolutionize analytics in high street retail?

 

Connecting with the customer

Internet of Things (IoT) devices are paving the way for “smart stores”, where interaction with consumers’ mobile devices can provide a responsive and immersive retail experience; one that tailors the store directly to a customer’s needs. Providing a customer experience that feels personalized and tailored to each individual is the Holy Grail for high street retail, and IoT provides tools that can take retailers closer than ever to providing a bespoke experience for each customer.

The line between digital and high street retail is becoming more and more blurred, as stores integrate their digital platforms with their in-store experiences.  Some retailers are offering their own apps or services such as on-the-go ordering, where customers can browse and order their purchases online, either using their computers or mobile devices, before picking up their order in stores. These apps can also provide features to enhance the in-store experience, such as maps or offers based on real-time information about the customer, coming from their mobile devices.

But when you start to integrate this omni-channel strategy into an IoT-ready store, things start to get really interesting.

The whole shopping experience becomes a two-way process, as almost every aspect of the retail environment becomes interactive. While the customers receive info from in-store apps, beacons in the store can alert sales associates of loyal customers, providing them with projections of the customer’s tastes and recommended purchase suggestions for the associate to give.

With these tools, staff can offer informed recommendations, to help improve the customer experience and drive brand loyalty.  All areas of the store can be transformed into marketing opportunities – for instance, traditional marketing displays no longer have to be static anymore, as digital signage can respond to devices and show personalized advertisements to users, based on social media data picked up from their mobile devices. Meanwhile, store mannequins can have sensors integrated that allow users to instantly look up the price and location of the outfits on display. Even the fitting rooms can be integrated into an IoT network, as integrated touch screens within the mirrors can allow users to quickly compare and search for alternative sizes and outfits without leaving their cubicle.

See everything

Once you’ve got the customers in store with your amazing, interactive retail interface, you’ve got to manage how they move through the environment.  Sensors can be employed to keep track of store traffic, allowing you to analyse where congestion issues occur, enabling retailers to drastically reduce the frustration and stress that a busy shop floor can bring to customers.

You can even go a step further and use this tracking data to optimize your in-store displays. By analysing where customers spend the most time in the store, you can position marketing targeted at them, located where it can be the most effective. Shelf display positioning can also become more refined, as you can position shelf displays where they are most likely to be attractive to the right customers. In the past, competition for shelf-space had been a sophisticated art, as different products compete for limited room. Now, analytics-informed positioning with data retrieved through IoT sensors can help towards turning shelving into a science, as the displays and positioning are tailored towards their ideal customer and can track eye movement, quantity of customers passing by and number of times products are taken from the shelves.

For instance, if a product kept being taken from the shelf for a customer to review, then was repeatedly replaced, it could be difficult for the retailer to gain any insight about the product if it had never actually reached the checkout point.  However with IoT sensors tracking when a product was taken from the shelf, how many were returned to the shelf vs purchased and how long customers stayed in front of the shelf, retailers can be much more analytical about their displays and identify what is and isn’t working.

The future is now

Some of this tech sounds like it’s been lifted straight out of a sci-fi film.  But these are features that are available to retailers now. The Aurora sensor is a device that allows for in-store traffic analysis to an incredibly sophisticated degree, so sophisticated in fact that it can exclude staff from its analysis for more accurate reporting. Companies like Offer Moments provide sophisticated digital billboards that produce personalized advertisements based on the location and demographic of those who approach them.

One of the most sophisticated high street experiences is actually here in the UK: The Pro: Direct sportswear store in Foubert’s Place, London (Retail Customer Experience). They partnered with Green Room Design and opened in 2014, boasting digital screens on almost every surface, interactive, digital mannequins, and responsive advertisements, all of which are fully interactive for the customer.

Bigger things to come

In truth, we are currently only in the infancy of integrating IoT into any aspect of our everyday lives, let alone into retail.  As retailers become more aware of the benefits of IoT integrated stores, the demand for the devices will increase. This will lead to the devices becoming even more sophisticated, which in turn will enhance the quality of the analytics the sensors can provide.

There is huge potential for IoT in retail, requiring just imaginative and creative applications of the technology that is available today.

 

Image attribution

Image provided courtesy of Takashi Kiso

 

We’ve just published a whitepaper on how Predictive Analytics is changing the retail experience, download it here: http://www.c24.co.uk/wp-content/uploads/2016/08/C24-Predictive-Analytics-in-Retail-Whitepaper.pdf

 

 

C24 is pleased to announce their latest whitepaper on Predictive Analytics in Retail.

C24 Predictive Analytics in Retail Snippet

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

Analytics-enabled Supply Chains: Predictive Analytics


How can analytics be used to optimize your retail supply chain to peak efficiency?

 

Beneath the surface

Retail is a lot like a swan or an iceberg: what we can see on the surface is just a fraction of the amount that’s going on out of sight. You might have the best in-store experience in the world, one that customers travel from all over the world to visit. You could have queues out the door of customer waiting to purchase your product. But if you don’t have a well-run supply chain, it can all be for nothing. All those people queuing up to buy your product will be spending their time queuing, not buying your products. With a well-oiled supply chain operation, you could be developing a way to reduce queues and customer wait times.

Analytics provides a means to not only keep your supply chain running, but to also provide predictions and projections that can allow you to tackle problems pre-emptively and run your operations at peak efficiency.

Meeting demand

Analytics is a fantastic way to ensure that you are getting the right amount of stock in rotation through the supply chain, to help the retailer make sure they are meeting demand, and are not wasting resources on excess stock. Food retailer EAT partnered with cloud-based analytics provider, Blue Yonder to reduce food waste by 14% (ComputerWeekly). They did this by analysing customer demand compared to external variables such as the weather, or local events for each store.

A similar strategy was implemented by the American department store chain, Stage, who utilized analytics of customer purchases to develop stock projections for individual stores based on the most popular sizes and styles of clothing (Forbes). Stage made sure that the shelf space was occupied as much as possible by clothing that customers would actually buy.

These optimizations don’t just mean that EAT are spending less money on wasted food, or that Stage are using their shelf space more efficiently. They are saving money on indirect costs, such as production, transportation, and storage of goods. If a retailer is only ordering the correct amount of stock to meet demand, then that means there is less excess being produced, which means that less transportation and warehouse space is required for both the raw materials and produce.

Targeted Efficiency

Once you’ve refined your supply and stock requisitions, you can start optimizing the individual components of the supply chain. At every step along the supply chain there are places where inefficiencies can occur. Analytics can not only be used to identify any steps where movement through the chain is slowing, but can even make suggestions on how to optimize the process. For example, data insights could suggest quicker routes and schedules for shipping, or could be used to optimize the flow of manufacturing processes.

But it doesn’t have to stop at optimizing the flow through the supply chain, data analytics can help to find potential solutions if there is a problem with the supply chain. Analytics can identify any potential external shocks to a supply chain, and then provide ideas about how suggested work-arounds may be implemented based on previous trends and data points.

Do you know where your stock is?

Data analytics enables companies to create real-time visualizations of their supply chains – letting them see the big picture of their data – through word clouds or dynamic graphs and charts.  In order to develop these real time data pictures, trackers can be used all the way along the supply chain, which can provide up to date progress reports of goods on their journeys.

As well as keeping track of your supply chain efficiency, these trackers can also be used to aid with quality control, allowing you to cut losses on spoiled or damaged goods. This can be especially beneficial for perishable goods, as their quality can be tracked in real time. With these tools, you can forecast shelf life in a much more efficient and accurate manner, and even predict when and where you could need a resupply.

Once this system is running at an optimal level, it could be completely automated, as the trackers send out reorders for goods as soon as they’re required. Rather than wasting time waiting for the low or spoiled stock to be noticed and the reorder sent out and processed, the sensors can detect when the shelves are getting empty, or when perishable goods are nearing expiration, and send out orders straight away.

Rapid response

In the past, calculating a single day’s costs for an entire supply chain could take over a week. Often, this is far too slow for companies to take effective action in responding to any raised issues or making quick changes that could save tangible cash. Thankfully, analytics can provide a solution. Intelligence engines can provide a calculation of a day’s costs to a degree of 99% accuracy, within a single day (Logistics Viewpoints). Along with real time visualisations, companies now have the resources available to react almost instantaneously to any issues. Problems can be tackled as soon as they appear, or even, in the best case scenario, before they even occur at all.

With these tools in place a retail supply chain can become a well-oiled machine, as goods glide all the way along from manufacturer, right through the supply chain and into the customer’s hands.

 

We’ve just published a whitepaper on how Predictive Analytics is changing the retail experience, download it here: http://www.c24.co.uk/wp-content/uploads/2016/08/C24-Predictive-Analytics-in-Retail-Whitepaper.pdf

 

C24 is pleased to announce their latest whitepaper on Predictive Analytics in Retail.

C24 Predictive Analytics in Retail Snippet

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

Only Stock What Your Customers Want: Predictive Analytics


How can analytics help retailers become more efficient and reduce waste in store?

 

Finding peak efficiency

Rare indeed is the retailer with empty shelves. In an ideal world, a shop would stock the exact amount of produce to meet the demand of every customer that walks through their doors, so that by the end of the day there’s nothing left in store and there are no customers coming in to find that what they want isn’t there. Sadly, this kind of system is nearly impossible and an empty store isn’t that attractive, after all.  Yet by utilizing predictive analytics, retailers can try and get as close as possible to this golden ration of supply vs. demand.

Waste not, want not

Waste can cost retailers a lot of money. Waste of materials, waste of stock, waste of time, they all add up to one thing: lost money. That’s even before you get into the ethical and environmental considerations that wasting goods can have. Therefore, reduction of waste should be a key performance indicator for any retailer. Utilizing predictive analytics effectively can allow retailers to sleep a little easier, without the big twin weights of losing money, and damaging the environment weighing on their conscience.

Waste is a particular concern when it comes to food retail. A lot of food is perishable, so if it isn’t sold within a certain amount of time, it is essentially lost money. In 2012, a study found that grocery waste amounted to an estimated 6.5 million tonnes in the UK alone. A more recent report, published in May 2016, found that most of this waste is avoidable, with a reduction of up to 42% being completely feasible (WRAP).

Food retailer EAT partnered with cloud-based analytics provider, Blue Yonder, to analyse the stock they sent to each individual outlet. In a short amount of time they were able to reduce their food waste by 14% (ComputerWeekly). They did this by aiming to provide the right amount and types of food that customers could potentially want, based on an extremely broad range of variables. They didn’t just look at the demographics in the areas around stores and previous customer purchases over a range of time, they went in-depth on data collected on a daily basis, analysing not only customer purchasing behaviours, but comparing them against variables like weather, holidays, what day of the week it was, and local cultural or sporting events. This provided EAT with an accurate projection of what types of food to stock in stores in order to meet demand, and what stock reductions they should make in order to reduce waste.

These kinds of projections can be applied to almost any kind of retailer, not just food. If your stock has an expiration date, either because it’s perishable or it might go out of style, knowing how much to stock can be extremely important to make sure it’s not going to waste.

Fitting your stock to your customer

Similar principles of waste reduction can also be applied to clothing retail. Unlike food, most clothing doesn’t have an expiration date (unless you count changing fashion trends). However, there’s another equally damaging waste of your time and resources: taking up storage and shelf space with unwanted goods. Once again, applying predictive analytics can solve this solution and ensure that you’re getting your stock exactly where it needs to be.

Summer in the UK means it’s festival season. Thousands of people from across the country travel to a few select locations to party and have fun. But because it’s the UK, there’s pretty much one inevitability about attending one of these festivals: you’re going to get rained on. Therefore, the wise festival goer makes sure that they have a good, sturdy pair of wellington boots as part of their standard festival equipment. So if you sell wellington boots, you make sure that you’re stocked up on them to meet supply and demand. Simple enough.

But say you run out of a certain size boot. You’re now automatically losing customers, as they have to go elsewhere to find that size. Meanwhile, you’re wasting shelf and storage space on boot sizes that no one is buying. Analytics can provide a solution to this. Rather than purchasing a blanket order of the same amount for each size boot, you can look at the demographics and previous purchases of customers to get a projection of which sizes are likely to be the most popular.

Of course, this principle doesn’t have to just be applied to wellies, it works with just about any type of clothing. American department store chain, Stage, have applied this principle to every individual outlet, tailoring their stock orders based on previous sizes and styles purchased (Forbes). This kind of in-depth analysis would take a massive number of employees looking at thousands of products across hundreds of stores to come up with the required data insights. However, with effective use of analytics, Stage was able to tailor each store to the specific demands of the customers in the areas around them, reducing the amount of unwanted stock that was taking up shelf and warehouse space, and ensuring that fewer customers had to go elsewhere for their purchases.

Striking at the right time with sales

But stocking what the customers want is only one aspect of getting customers into your stores. There is a huge amount of competition for just about any type of retailer, both on the high street and online. Sales and discounts are one of the oldest tricks in the book for getting customers into stores and driving footfall. However, you can’t just throw out a sale whenever you want: it’s got to hit at the right time to get the right return. This process of deciding when it’s best to introduce a sale or discount is known as markdown optimization.

In the past, quite a lot of retailers have left these kind of sales up to the gut-instinct of the store managers. However, Stage department stores decided to test analytics based decisions against store owner implemented sales in order to see which method was more effective. A lot of the time, sales are introduced in order to get rid of a season’s excess stock so that shelves are clear for new stock. Analytics software advised placing sales and discounts earlier in the season, just as demand was starting to dip. The results were conclusive: according to Steve Hunter, CIO of Stage, “90% of the time, analytics won.” (Forbes). Now, every store in the chain utilizes the analytics program to advise their mark down optimization process.

 

We’ve just published a whitepaper on how Predictive Analytics is changing the retail experience, download it here: http://www.c24.co.uk/wp-content/uploads/2016/08/C24-Predictive-Analytics-in-Retail-Whitepaper.pdf

 

C24 is pleased to announce their latest whitepaper on Predictive Analytics in Retail.

C24 Predictive Analytics in Retail Snippet

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

Customer Experience in High Street Retail: Predictive Analytics


Customer is key

Customers have a lot of options when it comes to retail. Not only are there numerous competitors on your average high street, but also online. How can high street retailers hope to compete with online stores, offering products available to be delivered the same day with huge choice?

Retailers need to look at customer experience. Excellent in-store experiences have been cited as one of the key influencers in enhancing customer loyalty, which is the lifeblood of any retail brand. The best way to achieve this is by helping customers feel like they are being treated like royalty; that the entire retail experience has been personalized to their exact needs and requirements. In the past this kind of service was only really feasible for high-end or luxury brands. Now, with the proper application of analytics, almost any retailer can provide a tailored service to almost any customer.

Stealing from the digital playbook

Online retailers have been providing personalized shopping experiences for years. Amazon, for example, will remember your previous purchases and where your most frequent order destinations are. It will make suggestions based not only on what you’re currently purchasing, but also on your shopping history from previous visits. The whole system is set up to make your shopping experience as efficient as possible, as well as encouraging you to make further purchases.

Pablo Picasso is widely quoted as saying “Good artists borrow, great artists steal.” The same works for retail strategies. Stealing a few of the strategies used by online retailers is well within the realms of possibility for high street retailers, and they can actually go further than websites in the services they currently provide

Rewarding customer loyalties with tailored service

There are already systems similar to online retail’s membership accounts available to high street retailers. They have existed since before not only Amazon, but the world wide web as a whole. Loyalty schemes have been an accepted part of the UK retail landscape for over three decades (Guardian).

In the past, loyalty schemes have been used to apply blanket discounts. However, with predictive analytics, retailers can acquire data from a customer’s use of loyalty cards to offer specific, tailored offers, based on not only the individual customer’s purchasing habits, but also the habits of customers with similar tastes. Customers are far more likely to respond positively and utilize discounts that feel personal to them.

Effective and appropriate suggestions from staff have been shown to contribute towards a healthy customer experience, an idea that Stage, an American department store chain, has implemented into the repertoire of their sales associates through analytics (Forbes). Although there were concerns that customers might feel this process was intrusive, focus testing and in store performance provided a mainly positive response.  Steve Hunter, CIO of Stage, stated that “[Customers] want our associates to be informed as to what they like, instead of offering an opinion out of the blue” (Forbes).

Sales associates can therefore attend to the customer’s individual needs and tastes.

Only the First Step

These are just some of the options currently being utilized by retailers. It’s likely we’ll see even more exciting opportunities and strategies emerge as technology advances and retailers allow themselves to be imaginative and experiment with different applications of analytics. Rather than being the death of high street retail, digital technology is in fact providing them with more and more tools to attract customers and provide an unprecedented level of service.

 

We’ve just published a whitepaper on how Predictive Analytics is changing the retail experience, download it here: http://www.c24.co.uk/wp-content/uploads/2016/08/C24-Predictive-Analytics-in-Retail-Whitepaper.pdf

 

 

Download the full whitepaper: Predictive Analytics in Retail.

C24 Predictive Analytics in Retail Snippet

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

C24 Publishes New Predictive Analytics Whitepaper


C24 is pleased to announce their latest whitepaper on Predictive Analytics in Retail.

C24 Predictive Analytics in Retail Snippet

What’s in the whitepaper?

We look at how analytics is changing the traditional shopping experience – and how in-store operations are being integrated with online e-commerce practices.

Why should I read it?

If you want to stay up to date with how analytics, and more specifically predictive analytics is influencing the retail experience, then download the whitepaper today to find out more.

Why has C24 written this whitepaper?

C24 is heavily focussed on business analytics – we have a product called Bi24 which we deliver to businesses across the country, especially to the legal sector who use the analytics tool to better manage their operations.  We also work heavily in the hospitality and retail sector, and see some of the technology coming down the line in the retail sector as a big opportunity for retailers looking to capitalise on big data within their organisations.

C24 Predictive Analytics in Retail Snippet 3

 

Big Data on the High-street


Improving retail customer experience through analytics

 

Retail has changed

The internet has had a massive effect on high-street retail. With customers able to order practically anything they desire online, often with next or even same day delivery, there is an increasing worry that physical retailers are starting to become a thing of the past. To counter this high-street retail needs to evolve. Gone are the days that retail can be seen as a simple exchange of money for goods or services. Customers expect a fuller “shopping experience”; one that offers something extra over simply using an online store. These experiences need to be developed in an effective and intelligent manner, which data analytics can provide.  Customer experiences can now be improved, altered and measured through big data.

Big data has been a hugely successful tool for online retailers and services, such as Amazon and Uber, and there’s no reason it can’t be implemented for high-street retailers as well, to improve their services and better align their resources.

What can Big Data do for retailers?

Big data can serve a variety of roles in retail, both in developing new strategies or even updating some classic marketing tools. Coupons and sales have long been a staple of a retail marketer’s arsenal, and have been shown to have a measureable effect in getting people into a store. But it can be difficult to measure the actual effectiveness of these discounts in generating additional revenue once the customer is in store. By analysing historical data, analysts can create models of what could have happened if the discount scheme was never introduced and run these models side by side with real-time analytics.  What about if a coupon had been released on a sunny day, in mid-June compared to what would happen if no coupon had been released on a week day in February when it was raining?  Would a coupon deliver better results depending on the weather, football fixtures, location or recent news stories?

Retailers can now weigh up the gains of the new purchases against the loss of revenue produced by the discount. Businesses can then adjust their discount strategies in an effective manner, ensuring that they are used to their maximum benefit.

Analytics can also be used as a form of crowd sourcing. Rather than relying on focus groups or anecdotal reports, retailers can identify in real time the marketing language, displays, and pricing that is most attractive to both current and potential customers, and adapt their offerings accordingly. Furthermore it can be used to uncover the key issues that might cause a loss in customer loyalty, which is essential to any successful business. It is well documented that the cost of acquiring a new customer is up to six or seven times more expensive than retaining a current customer, so anything that can aid in retaining customers will be a massive advantage to any retailer.
The Big Picture

On a larger scale, data acquired at the Point of Sale (POS) can be used to develop national or regional strategies for retailers. Identifying top selling stores and the demographics that purchase from there can allow retailers to effectively target the right customers. It can even go beyond this: the physical positioning of these stores can be analysed to best identify where a store should be located for maximum gains i.e. Are the stores freestanding or outlets in shopping centres? How close are they to competitors? How are the stores laid out to create an easy shopping experience?

Historical data can be used to develop forecasts for order quantities at every level, from national forecasts right down to at an individual level. For example, in the past a clothing retailer might have seen an increase in swimsuit purchases in the summer months on a national level. Rather than simply increasing the quantity of swimsuits available in every store across the country, appropriately analysed data allows them to see which individual stores are reporting the highest sales increases and adjust their orders appropriately, allowing for more intelligent and efficient management of stock.
How to get the data

An issue facing physical retailers is simply acquiring the data needed in the first place. Perhaps the simplest way is just to follow online retail’s example. Most online retailers require creating an account to shop with them, which instantly provides them with customer data. Similarly, an in-store loyalty scheme (with incentives to attract customer engagement) can allow retailers to acquire data on an essential core of loyal customers. Several retailers have even began offering digital receipts, sent to a customer’s email at the physical checkout, allowing for easy acquisition of customer data.

Perhaps the most important strategy is offering an “omnichannel” retail experience. Customers in the 21st century like to do their research online now before purchase, even if they eventually end up going into a physical store. This can take a variety of forms, from providing instore Wi-Fi that requires customers to log-in with email and details, to a fully furnished app developed to enhance the in-store experience, but also acts as a way to acquire customer data for the retailer.

Finding the right tools

However this is all reliant on retailers being adaptable and intelligent over how they use data. Recent studies by eCommera found that 23% of UK retailers can’t make sense of data in order to make appropriate business decisions. 50% of retailers believe that they don’t have the correct intelligence and analytics tools to suit their needs, and only 16% have confidence in their data analytics to provide organizational insights. This just shows that retailers need to do their research to find the right tools for their needs.

Adapt and thrive

As customers become more and more tech savvy, retailers need to evolve too. With such a vast amount of competition out there, both from physical and online rivals, the high-street retailers that are going to not only survive, but also thrive, will be the ones that acquire, analyse, and utilize Big Data in intelligent and innovative ways to further the customer experience.

 

References

Business Solutions Monthly Big Data Analytics: Real World Benefits for Retailers

Computer Weekly Are retailers using data analytics to their advantage?

WWD POS Data: Retail’s Biggest, ‘Untapped’ Opportunity