How is Artificial Intelligence Shaking Up Retail?

pexels-photo-40120

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/

 

Are shipping fees costing you customers?

Startup Stock Photos

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?

thesaleswayvlog3

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

 

Virtual Reality: The Next Step for Business Intelligence

virtualreality

Everything is being virtual realit-ified – from product development through to buying clothes.  And it’s now being seen as the next step for Business Intelligence and Data Analytics.

Even simple data visualisation tools are now enabling businesses to make sense of their data – which often runs to thousands of lines of spreadsheet data.  It’s impossible to get a handle on trends and patterns when data is represented in rows, and too big to fit on one screen.

But when turned into graphical representations where trends can be understood in seconds and insights gained in moments, the value of data visualisation becomes clear.

So taking that to the next level and creating a more ‘immersive’ experience is the natural next step.

Plans are underway within data analytics organisations to create applications that enable Data Scientists to literally step inside their data.  They will be able to see the data represented visually in 3D; they can walk around it, stand under it, move it and see it from lots of different angles to better understand what is happening.  They can layer over other data points – over and over to see correlations between information; which is usually limited in a 2D data visualisation tool where it soon gets messy and complex to understand once multiple different data points are overlaid.

Product development departments are already starting to look at how virtual reality can be integrated into their own data workflows, to reduce time spent developing and be able to interact with physical products without the cost or time of developing models first to spot errors.  This offers a great saving opportunity, but also the possibility to explore many ‘what-if’ scenarios that might be too costly to explore with physical products.

 

The world of data is changing

As many businesses now employ Data Scientists, it doesn’t make sense to use the same old tools to do different jobs.  Virtual Reality will provide a new way of visualising and engaging with data.  If we can uncover insights quickly from just seeing a pictorial representation of data, just think what’s possible when you take that into a 3D environment where you can ‘walk through’ information.

The Wall Street Journal has a good example of a Virtual Reality-esque visualisation of the stock market history: http://graphics.wsj.com/3d-nasdaq/.  This helps to give a bit of a flavour of what could be to come – imagine if you could stop on your journey and delve into the detail of a particular year, see a video of reporting at the time in the background while in front of you is a number of visual graphs showing different info that you can move around effortlessly, layering up to gain insights.

It’s a more intuitive and immersive form of data analytics – and I can see how stock markets would benefit from having all of their data in a virtual reality environment that traders use instead of desktop screens.  Or perhaps instead of wall mounted monitors showing tech support graphs, IT technicians will be wearing VR headsets – analysing server capacity and alerts in real-time with virtual reality versions of their remote customer datacentres in front of them to help resolve issues – even when they’re not there in person.

It also offers the opportunity to be more collaborative when analysing and reviewing data analytics – imagine being able to walk your entire Board of Directors through a virtual reality data visualisation of your financial performance to date.  Or perhaps you offer customers the opportunity to understand the data results from their recent Quarterly Business Review with your company via virtual reality headsets instead of emailing over a quick report they probably won’t even open.  Some companies are even developing virtual reality chat assistants to bring an in-person experience to online engagements.

Underneath it all, the aim is to get from complex lines of data through to actionable insights sooner.  And virtual reality certainly looks like it has the power to transform how we engage with data.

 

 

References:

http://fortune.com/2016/09/08/virtual-reality-vr-industries-application-examples/

http://www.forbes.com/sites/forbestechcouncil/2016/07/22/how-virtual-reality-will-impact-businesses-in-the-next-five-years/#7119ea152241

http://pwc.blogs.com/analytics_means_business/2016/08/5-reasons-to-use-virtual-reality-for-data-visualisation-.html

http://www.forbes.com/sites/bernardmarr/2016/05/04/how-vr-will-revolutionize-big-data-visualizations/#2d7e910c4ac5

HPE and Verteda Video Released

c24video

Have you seen our recent case study video by HPE and Verteda?

If you prefer to read it, then access the case study here, otherwise take a look at our video below with Matthew Prosser from Verteda and Lee Duffield from C24.

Why Rio 2016 is both a problem and an opportunity for your data project

c24-rio-2016

The data visualisations that we all see online and on social media are making us more open to using this kind of information at work.

Our daily lives are now drenched in data, delivered to us from our televisions, our computers, and the smartphones in our hands. Charts and tables are commonplace, but the online world and social media have also made infographics a powerful tool for the presentation of this information, especially when coupled with images, animations, video clips and written commentary. This data can come from a huge number and variety of sources, brought in from places all around the world.

A perfect example of this is what we have seen in the coverage of the Rio Olympics. Online news providers have taken great leaps of the imagination in how data can be delivered to us, harnessing the tools at their disposal. The Guardian is a prime example, with their coverage of Great Britain’s cycling success over Australia that saw Sir Bradley Wiggins win his fifth Olympic gold medal (1). The newspaper created an animated version of the race that showed what happened every second of the way, with the reader being able to click through each stage at their own pace.

Not to be left trailing in second place, The New York Times has drawn up an extensive set of data visualisations that shows exactly how well each country has done at each Olympics since the games began (2). The graphics are a brilliant mixture of aesthetics and information, delivering a huge amount of complicated data at a glance.

These high-tech ways of accessing data are becoming everyday experiences for many people, but how does this affect businesses beyond the mass media outlets, and should companies strive to access and make use of these new tools?

There is a danger that if some data analytics projects are at a fairly embryonic stage they could seem outdated by the time they’re implemented. After all, with online trends changing day-by-day, what seemed like a great idea just a few months ago could be old-fashioned by now.

This poses problems for business who are then pushed to keep up with the latest innovations but don’t want to shake-up their operations. However, there is a good chance that your staff are using better, newer technology at home than they have access to when at work.

There are echoes of the BYOD (Bring Your Own Device) phenomenon, where the smartphones that people were buying with their own money were far more advanced than the ones they were being given by their employers. BYOD was a clever way of working around this without companies having to regularly shell out for new phones.

Now your staff will be using the data analytics power of social media such as Twitter and LinkedIn in their personal lives, along with gathering data on themselves with mobile apps such as Run Keeper. It is commonplace to have data at our fingertips, and people will be happy to use equivalent tools at work.

It would be very easy for anyone in the position of running a company that is making use of data visualisations to look at the sort of tools that are being used elsewhere and become despondent at what they have at their disposal. Adopting new technologies can be expensive, and many employers could worry that the changes this can bring to a workplace could have a negative effect.

But what needs to be remembered is that any new data technology that is brought in will probably not be unfamiliar to your colleagues, and may be something they are already using in their everyday lives. With so much technology, and so much data, now available to each and every one of us, that new piece of software that you’re apprehensive about buying may not have the disruptive effect on your team’s way of working that you think, and could give a massive boost to your profit margins.

What’s also very important to remember is that data visualisations are only the endgame of a very long process, one that begins with gathering good quality data itself. While new tools designed to present this data are emerging all the time, the basic foundation that they build upon is information. And if you’re looking at new ways of visualising data then you probably have a good bedrock of this information at your disposal already.

There’s an opportunity here, a massive one, that could see your company pushing itself to the forefront of the way in which data is presented and getting everyone involved in its use, not just data scientists and your IT team.

What’s needed to make the most of this is the realisation that the apps, websites and social media that your staff are using on a daily basis are indicative of a wider acceptance among them of how data now works in our world, and how it touches every aspect of our lives. People are now comfortable with digesting huge amounts of information, and even expect it to be delivered to them. If they do this in their own time, they’ll have no trouble doing it at work.

 

 

Image provided courtesy of Ian Burt

Infographic guide to Predictive Analytics in Retail

c24-predictiveanalyticsinretailpic

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

 

Are You on the Data Offensive or Defence?

c24-data-defence

Understanding the different types of data positions – data offensive or data defence.

 

Companies are either on the data offensive or data defence – and organisations need to move to being on the offensive to actively take hold of data and make tangible use of it.

There is a huge amount of data that any company will gather over time. This can be deliberate, and be something that you have set out to obtain, or it can be something that simply gathers as a result of the IT systems that we all use.

There are two ways a business can approach this data, and it’s a choice between a position of defence or offensive. One could hold you back, but the other is much more positive, allowing you to push your company into new areas and target your approach so that you achieve exactly what you need to.

 

The defensive approach

Data defence is the traditional approach to managing the information that your company holds. It’s all the regular things that have to be done with large amounts of data, such as maintaining security to make sure none of it leaks or is compromised. It’s the governance of data, the everyday handling of it and the processes around it.

This also includes ensuring privacy and making sure that the quality of the data is up to scratch. These are certainly things that have to be done with data that is gained in a commercial context, and many of them are done to make sure that your business falls in line with whichever set of regulations you have to adhere to.  It’s a case of preventing data from becoming a problem – rather than seeing it as a valuable asset.

This is the approach that many companies take towards data, and the one that can seem to be sensible and correct. That is, until you look further than the data defence attitude and closer at what could otherwise be done. There are opportunities to take the data that you have and use it to push your business on to the next step.

 

Go on the offensive

Being on the data offensive is about taking the wealth of information that you have at your disposal and exploring the possibilities of what it can do for your business in a proactive sense. Whereas data defence is about making sure that everything is in order, data offensive sees you pushing the boundaries and creating new opportunities.

The data that you have at your disposal can open doors for your business that were closed before. This information can support marketing and help to target outbound campaigns, making sure you are reaching the right people in the right way. In turn, this helps to build new revenue, all of which can lead to further data being gathered as time goes on.

Data management can be at the forefront of your company’s strategy rather than being something that simply has to be done. In the modern, digital world the companies that are using data well are those that are harnessing its power and using it to change their behaviour and the way they work. Data is driving their behaviour and they are allowing it to take the lead rather than letting their existing behaviour govern the way data is collected and protected.

 

A light in the dark

There is another kind of data out there that might not seem so full of opportunity until it is put under the microscope and given a closer look.

Dark data, as it is known, is the information that tends to be ignored by businesses and just builds up in the background over time. This could be server logs, data about old employees, and outdated login information, for example. In his book Dark Data: A Business Definition, Isaac Sacolik describes it as “data that is kept ‘just in case’ but hasn’t (so far) found a proper usage.” (1)

Much of this data will be seen as having little or no value to your firm, and simply something that is given the minimum amount of attention to make sure it is secure and stored correctly. But harnessing this data can be a big step in the process of moving towards data offensive and taking your company forward.

Any business that finds itself in possession of a significant amount of dark data needs to look at how to harness the opportunities that it can create, and how to capitalise on that information and turn it into something proactive rather than letting it impact your business’ resources.

While dark data can be turned to good use and create opportunities, the failure to do this could pose a risk to your company. Instead of letting it become a burden on your business, why not turn dark data into something positive?

Most companies are currently stuck in the data defence approach, but there are new solutions to this problem that can put you on the offensive. Dark data could be the key to where you go next, helping you to explore new avenues that you hadn’t thought of before. This approach will become even more effective as data analytics tools become standardised and the ability to pull information from the unlikeliest of sources increases through technology such as IoT sensors.

There is a wealth of information that any company builds up over time, and the choices are either to let it become a drain on what you do or harness the power that it can give you and allow it to take you forward.

 

References:

 

 

Image provided courtesy of KamiPhuc

 

Smart Stores of the Future: Predictive Analytics

C24 Predictive Analytics Whitepaper 4

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

C24 Predictive Analytics Whitepaper 3

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