When it comes to using Artificial intelligence (AI) and Radio Frequency Identification (RFID) in retail for process optimisation, the majority of use cases involve management or ‘HQ’ level decision making. These include automating functions such as store planograms and stock optimisation between stores.
However, AI can also impact retail on a much more micro and everyday level, actively assisting store staff in one of their most common daily routines –stock replenishment.
Using RFID and the information it collects from stock reads, we can produce AI pick lists to optimise and guide staff through the replenishment process. Not only are we combining RFID technology and AI algorithms to produce these pick lists, but existing RFID processes are already assisting staff. When you put all this together, replenishment become a walk in the park.
Let’s start at the beginning…
RFID-based Stocktake and Replenishment – The backbone of modern stock management
With RFID, store staff can do regular (often daily) cycle counts of the entire store quickly and easily. This is simply done by walking around the backroom and salesfloor with a handheld reader that counts items that are several feet away, using radio frequency. An RFID application or software, like the Detego platform, will then compare the actual stock levels of the shop floor with the desired counts (i.e., planogram), and tell staff exactly what needs to be replenished from the backroom.
So far, what has just been described has been entirely RFID-based and is the standard process for RFID in retail. This is already far easier and more accurate than traditional methods, not to mention the actual effect of the technology like higher stock accuracy and product availability. But why stop there?
Taking it one step further – AI pick lists for ‘mapping’ the perfect replenishment path
Normally, even with the support of RFID, the store staff are then left to fulfil replenishment by themselves, using the list provided by the application. These pick lists are often only sorted by product features such as name or price. Because back rooms can be quite large in bigger stores, or densely packed in smaller ones, the staff’s ‘pick path’ can be incredibly sporadic. This is made even worse in the case of new staff who don’t know the layout of the backroom by heart, or even experienced staff if stock has simply been moved around and updated with the start of a new season.
By utilising new tag localisation techniques, it is now possible to locate where items are in the backroom in relation to each other. This is done during the regular RFID stocktakes that are already taking place, utilising data mining and machine learning pipelines without any need for additional hardware or specialist tags. Using this information, we can create automated AI pick-paths that, using a mobile application, guide staff through replenishment and present the most efficient order to collect items in.
The above example is designed to present the quickest possible replenishment route for staff, so is solely using items’ distance from one another to calculate a pick list. However, AI pick lists can process the replenishment list in a number of ways, depending on what the store wants to focus on.
Replenishment paths could take additional factors such as product value or expiry date into account, alongside the location of the items. It would then look for items that fit this rule and are nearby one another in the backroom. For example, a pick list targeting on-floor-availability would group nearby items that are running low on the sales floor, so that these items are refilled first to speed up the replenishment process whilst also combatting loss of sales from out of stocks.
Benefits of AI-pick lists
Artificial intelligence (AI) is becoming increasingly utilised within the retail industry. One of the main challenges with the technology is having enough relevant data to be utilised or ‘fed into’ an AI system. With Radio Frequency Identification (RFID) providing huge amounts of accurate item-level data for merchandise, the two are a match made in heaven.
This webinar cover AI’s applications for stock optimisation and how machine-learning can ensure products are always in the right place at the right time, including:
- AI-driven, automated planograms for optimised product availability
- Visual merchandising and ‘Money mapping’ in stores to monitor and increase sales.
- How RFID stock-takes and AI pick-lists combine to make replenishment faster and more accurate than ever
- How machine learning smart fitting rooms are bringing accurate cross-selling into the physical store
- Using AI for demand prediction and stock optimisation across store networks
Digitally native competitors and demanding customers are forcing a new perspective in retail. Artificial intelligence and machine learning have huge implications for technology and is one of the main driving forces of the ‘fourth industrial revolution’. The AI in retail conference explores how this fast-emerging technology is changing the retail landscape.
The event takes place on the 16th of October at Cavendish conference center in London (W1G 9DT).
With speakers from several major retailers and brands including Sainsbury’s, Domino’s, Microsoft and google, as well as our customers Levi’s, the conference promises to be incredibly insightful on the practical applications of AI in retail.
Presentation: AI for inventory and stock optimisation – ensuring the right products are in the right place at the right time
Our senior data scientist, Simon Walk, will be discussing how the Detego platform offers AI capabilities to retailers as part of a SaaS solution. The presentation will cover AI’s applications for stock optimisation and how machine-learning can ensure products are always in the right place at the right time, including:
- How Artificial Intelligence works for stock optimisation
- AI-optimised planograms – How AI and machine learning can be utilised to produce more up-to-date and relevant planograms that are optimised for each individual store
- Machine-Learning Product recommendations – How AI is used to provide superior product recommendations to customers to increase sales, and how Detego utilises this technology in smart displays to bring cross-selling to brick-and-mortar stores
- Smart Picking Lists – How AI can take stock replenishment to the next level by guiding staff through replenishment with optimised ‘picking paths’ that calculate the most efficient replenishment route for store staff
Who should attend the AI in retail conference?
The event is aimed at senior executives within retail in the following departments:
- Digital Transformation
- Data Science / Data Engineering / Data Analytics
- Sales & Marketing
- Customer Services
Coming to the conference and want to schedule a meeting? Get in touch below:
The retail industry has been subject to enormous change in recent years, a trend that looks likely to continue even now retail has found its feet in the digital age. This significant shift in the landscape coupled with the collapse of numerous household brands naturally created something of a panic in the industry and a fear an impending ‘retail apocalypse’. So, has this taken place and are brick and mortar stores heading toward extinction in the age of online shopping? The short answer: no. Retail sales, in general, are increasing, and whilst online sales are certainly growing at a faster rate than in stores, in the UK market Brick and Mortar stores make up roughly 84% of all sales, a trend that applies globally.
In fact, with the help of new technology, in particular, AI brick and mortar stores can take lessons from e-commerce and bring new innovations into the physical store. A study by Capgemini found that around 68% of pure-play online retailers have implemented artificial intelligence in some fashion, compared to only 10% of brick and mortar stores. Utilising new technology can often be the key to growth, so perhaps it’s no surprise that online retailing is growing at such a faster rate. This is beginning to change however, as physical stores are learning to adapt.
AI-assisted cross-selling in the store
The ability to cross-sell using AI is a huge strength of online retail. Online stores use machine learning to make intelligent and tailored product recommendations to customers while they are shopping This naturally increases sales, but does so relative to the quality of the product recommendations, hence the use of AI to perfect the process and make more successful recommendations. Brick and mortar stores have traditionally never been able to make use of cross-selling like this, having to rely on in-person customer service to drive sales. But as technology has improved, they can now make use of both.
The main reason for this is the emergence of chatbots as a mobile platform for in-store product recommendations. Whilst chatbots themselves originate from online, certain retailers now implement them in their stores to assist customers. Their primary function is often to assist customers if store associates are all occupied. Chatbots can help customers locate items, find more information on products and check stock availability, all through their smartphones. Intelligent AI-powered chatbots will then be able to recommend products similar to the item’s customers were searching for, not only increasing sales but improving the customer experience.
The fashion industry has taken this concept even further with the use of smart mirrors. Smart mirrors use advanced tagging technology (RFID) to sense the items that a customer brings into a fitting room. It will then display the products on the mirror and, like a chatbot, assist customers by providing information on other available sizes and colours and recommending products that are often bought with the ones being tried on. Most smart mirrors also have a feature that calls store staff to assist the customer, either providing face-to-face support or bringing the recommended items the customer has chosen to the fitting room. Smart fitting rooms are the epitome of the merger of the digital and physical in brick and mortar stores.
AI-powered in-store business intelligence
Artificial intelligence can also go a long way to revolutionising traditional brick and mortar store processes. One of the best examples of this is the use of AI assisted planograms. A planogram is essentially a plan of where items should be displayed on a shop floor to maximise customer purchases. In certain sectors such as fashion, planograms must be even more detailed and include the optimum display quantities of each different size and colour.
Artificial intelligence can revolutionise the planogram, using machine learning to constantly optimise not only the positioning of merchandise but the most efficient quantities of different articles to display on the shop floor. The advantages of this process being performed through AI are huge. Not only does it largely automate the process, saving time for staff, but it will constantly adapt and improve and can personalise and optimise the planogram on an individual store level.
The future of retail, online and in-store
To conclude, the impact of digital technology on the retail industry, in particular brick and mortar stores, has been significant but not actively catastrophic as many feared it would be. The emergence of ecommerce has gone from a threat to a strength for some physical stores. Not only has omnichannel retailing (something we did not have time to explore, but we go into detail on here) allied the different methods of shopping, but brick and mortar stores have now begun to incorporate certain technologies from online.
AI technology, originally something only online retailers could really utilise, is now finding its way into brick and mortar stores and improving both store processes and customer experience. We are also just scratching the surface of AI’s uses in retail and as more retailers choose to adopt the technology more benefits will be discovered. So, in reality brick and mortar stores are far from going extinct; they are in fact evolving and will continue to do so.
Retail software specialists, Detego, have presented their ground-breaking methodology for in-store product recommendations, helping bring the same quality of cross-selling over from e-commerce and into the physical store. The new AI-based recommendation engine will enable retailers to provide personalized product suggestions utilizing data unique to store locations and point of sale information, without the need for identifying customer profiles.
Cross-selling through related product recommendations has always been a huge strength of e-commerce, with 35% of Amazon’s revenue generated by its recommendation engine (source). In recent years, innovations in RFID-based solutions such as smart fitting rooms and mobile chatbots have opened the doors to automated product recommendations within physical stores. Whilst the technology is now available, there is still one more hurdle between Brick and Mortar stores and effective cross-selling. This is namely the fact that the best recommender systems require vast amounts of both personal and aggregated data to provide effective suggestions, and whilst this is at a surplus in e-commerce, physical stores traditionally struggle with data being limited as well as sparse.
Speaking at the ACM UMAP 2019 in Cyprus in June, data scientists from Detego, who specialise in RFID-based software solutions for retailers, presented their proposed method of data-manipulation for in-store recommender systems with a paper titled: ‘Beggars Can’t Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores’. The approach involves an alternative algorithm that leverages shopping-baskets and common-item combinations combined with point of sale information. Detego says this allows retailers to provide targeted recommendations with a 6.9% increase in quality, aimed at individual stores, without having to maintain separate models for each location. When combined with the technology to deliver these product recommendations, retailers could see a substantial increase in sales in Brick and Mortar stores, whilst customers will see a more connected and engaging in-store experience, as Detego continues to bridge the gap between online and the physical store.
“Customers who bought this also bought…” is no longer a phrase reserved exclusively for customers of e-commerce platforms. Due to the adoption of RFID-based technologies, such as Detego’s Smart Fitting Room, personalised recommendations can also be presented to customers of brick and mortar stores. Moreover, Detego’s AI-based recommendation engine is tailored towards the specific requirements of fashion retail stores, such as fast-changing and varying product assortments.’ says Matthias Wölbitsch, Detego data scientist.
With Detego now successfully rolling out the Smart Fitting Room application alongside their real-time inventory management software, this latest improvement is another opportunity for retailers to evolve their stores for the future.
Planograms: Planning and implementation
Planograms aim to optimise article availability and thus specifically stimulate sales. The term planogram refers to two aspects: on one hand the actual merchandising, i.e. which articles are presented on the sales floor and how, and on the other hand, the detailed quantities for individual colors and sizes in order to meet customers’ demands in the best possible way.
Ideally, merchandising and planogram go hand in hand: customers are inspired by the form of presentation and then, their desired product is available in the matching size. The reality, however, most often paints a different picture.
Planogram – Merchandising
Planogram – Quantities and Priorities
Visually appealing and available in relevant sizes
Breaking this down, there are two questions that retailers face: 1. How to define a planogram for my stores with a suitable size distribution? and 2. How can we implement an efficient refill process such that the plan is properly executed?
With AI (Artificial Intelligence) and RFID-based processes, Detego InStore helps to answer both questions by producing AI planograms. Since the manual maintenance of the planogram per store can be enormously time-consuming, we rely on machine learning procedures to define a precisely optimised size distribution for all articles across the store. Not only does this save an enormous amount of planning time, but it also addresses the ongoing dynamics in individual stores. The self-learning system adapts to possibly changing conditions and continuously optimises the plan.
During the operational process in the store, Detego InStore also supports the store personnel at several occasions: The software offers two parameters that provide information about article availability at any time and therefore represent important KPIs:
- On-floor availability: The percentage of all available articles that are currently displayed on the sales floor
- Planogram compliance: Provides information on how well the planogram with its individual size distribution is implemented on the sales floor
If one of the two parameters fall below certain threshold values, store staff needs to action: In addition to classic ERP systems, Detego InStore offers a finer level of granularity in the stores, by telling store staff that certain articles are available in the backroom but not on the salesfloor and therefore need to be refilled to comply with the predefined planogram. Retailers benefit from a complete process for the planning and implementation. Another advantage: Refill advices in the app are sorted such that the search in the back room is made as efficient as possible by minimising walking routes.
With AI planograms, shelf space is used for top sellers and is not wasted on sizes that are rarely or never bought. With its self-learning components, the Detego platform for the store makes a suitable proposal for all sizes and facilitates implementation in daily processes – including relevant KPIs for measuring performance. And if a certain size is not available in one store, the platform offers an exact inventory view of surrounding stores – ready for click & collect.
Benefits for retailers:
- Individually optimised AI planogram per store
- Efficient use of shelf space according to bestsellers per store
- Guided processes: from planning to refilling
- KPIs to provide insights on operational excellence per store – in real-time
Benefits for consumers:
- High on-floor availability for the locally popular sizes
- Positive customer journey
- Overall increased article availability through exact inventory data on the entire store network – including reservation options
There’s a lot of talk about artificial intelligence at the moment, but not that many practical use cases, especially in bricks-and-mortar retailing. We should know. We were one of the early adopters of machine learning in the development of our retail software and have launched a popular chatbot tool to help consumers get quick answers to simple stock enquiries without having to seek out a sales assistant or call customer service.
Aside from a general foreboding that there’s more talk about AI than action, and that machines might eventually outperform humans, we get the distinct impression that most retailers are still trying to understand the basics about what machine learning can actually do for their business and how AI will help with consumer engagement. So, here’s our attempt to unravel the mysteries behind the topic of artificial intelligence in retail and see whether it’s a passing fad, or something retailers should be paying more attention to.
Knowing your product
There is one field where artificial intelligence has already made a significant difference, and that’s getting to grips with vast amounts of data and making much more informed product recommendations. It’s a technique that was spearheaded online by the likes of Amazon – making suggestions about what other products you might like – but still has a long way to go on the high street. There just isn’t a very joined-up approach between the worlds of online and bricks-and-mortar retailing. And the availability of products is often neglected; not many retailers take into consideration what products would be best (or most profitable) to shift.
That’s where AI comes in.
Artificial intelligence, by amalgamating lots of data and making decisions based on a variety of factors – product availability, purchase history, current trends, profitability, and so on – gets better and better at making reasoned choices. All this might sound obvious, but it’s something very few retailers actually do in their stores. Most retailers still have rather disjointed processes across various channels and different departments suffer from a lack of data input on a consistent level. Some processes are semi-automated, but many – such as merchandise planning and product assortments – remain largely manual. For example, buyers still base most of their decisions on out-of-date sales figures and gut instinct, rather than using much more efficient machine learning tools.
Artificial intelligence is ideally suited to forecasting and stock allocation. These processes historically tend to be quite manual and cumbersome and generally are not managed that efficiently – largely because it’s just too much work to find the ideal mix. It’s something that’s typically done by relatively small departments, even though product selection and stock availability are clearly so fundamental to a retailer’s bottom line. Yet self-learning mechanisms can be put in place to maximise availability and promote what’s most likely to sell.
In most stores today, store staff are prone to stack shelves based on whatever available sizes there are and what will fit, rather than having technology that knows what will be best for that particular store’s profit. By using AI, we’ve found that different stores, even in the same town, might require completely different sizes of garments to maximise their sales.
Coupled with RFID tags on every item to help monitor stock with near hundred percent accuracy, artificial intelligence can be used to improve on delivery performance and the distribution of inventory between stores, the warehouse and even to the consumer. For example, we found one retailer flummoxed by hundreds of cartons of products having been shipped but never received and various departments spending a long time trying to resolve – something a machine could unravel in seconds. It’s not uncommon for retailers to not know exactly where stock is at any particular time, but the joy of artificial intelligence is ensuring that human mistakes are minimised and promises – such as a marketing campaign offering the latest product release at a special price in chosen stores – are always fulfilled.
While there’s still a lot of hype about chatbots at the moment – robots to help with customer service – and undue pressure to adopt some of the latest (and arguably more advanced) techniques inherited from the online retail world, this is just the start of what can be done regarding artificial intelligence in retail.
Chatbots, available on people’s smartphones for asking advice when they enter a store, can give an instant link to customer services and access to a much wider catalogue of products and services. The appeal of chatbots has not only come about because young people, in particular, are now used to dealing with them online; but also because a lot of people don’t really want to have to track down a sales assistant in a busy store and then get the ‘don’t know’ or ‘don’t care’ response that can ruin a retailer’s reputation. Simple, product-related tasks and stock checks are ideally suited to machines. We’ve even found sales conversion rates to be up to five percent higher in stores where we’ve introduced chatbots with AI-powered product recommendation tools.
Clearly, chatbots won’t ever replace humans working in stores. But they can certainly complement them: often proving more reliable; never prone to sickness or breaks; not trying to drag you into a conversation and oversell, and always available 24/7.
Increased stock transparency and profitability, as well as better customer engagement, are what will help keep retailers flourishing – allowing them to compete with the increasing threat of online retail goliaths who are setting the trends for the future of shopping. Artificial intelligence isn’t just another fad. It might seem like fiction, but it’s clearly here to stay, outperforming expectations all the time.
Retail software specialist, Detego, will showcase three of its software solutions at EuroCIS, taking place from 19 – 21st of February 2019 in Düsseldorf, Germany. Visitors to Europe’s leading trade fair for retail technology can experience Detego’s market-leading solutions for the fashion retail industry, including real-time inventory intelligence and pioneering IoT and artificial intelligence (AI) projects. The live demos being showcased will be part of an impressive booth setup (Hall 9, booth #C04) which showcases the future of physical retail stores.
For inventory management of the store, Detego offers the InStore Lean Edition which meets the demands of the fashion retail industry for a quick-start solution into the digital store. Offered as a SaaS (Software-as-a-Service) solution with cloud hosting, retailers benefit from high inventory accuracy and consistent article availability at low cost. This is possible through fast and accurate stocktakes and automated replenishment processes, bringing long-term benefits to fashion retailers and most importantly, delivering quick results. The app-based solution can be active within hours and has a clear path of scaling to ensure rapid deployment and an easy functional extension to the Detego InStore Full Edition. “More and more fashion retailers are living by the mantra of ‘keep it simple’ for their RFID implementation which is exactly why we successfully implemented across the world the Detego InStore Lean as an ‘out-of-the-box’ SaaS-based RFID solution.” Says Kim Berknov, Executive Chairman of Detego.
The fitting room is the most important place in the entire store when it comes to purchasing decisions being made. This means that it’s crucial to provide support and additional services for customers. Detego’s Smart Fitting Room provides product recommendations, such as matching items available on the sales floor that can be brought directly to the fitting room by sales staff via a “call-to-assist” button. The customer can gain access to the benefits of e-commerce by looking up other products, checking for availability, reserving articles or having them delivered directly from the store to their home. Links to videos or social media feeds can also help. These value-adding services are what continue to drive consumers to shop in-store.
Answering as many questions as possible for shoppers through AI, Detego’s chatbot supports the sales personnel and bridges waiting times for customers until there’s a salesperson available for individual service. The virtual store assistant accompanies customers throughout their entire shopping experience and provides additional support in the vital decision-making process, such as making product recommendations and earmarking other popular items, bestsellers, discounted articles or product variants. The AI capabilities are not just limited to the chatbot itself, but also work continually in the background by communicating with other systems in the store. The chatbot checks the actual availability of articles and compiles data about customer types and their preferred article combinations and choices. As a result, recommendations become more meaningful and personal and only articles immediately available in the store are recommended.
“We want to introduce fashion retailers to solutions that support them in their daily challenges,” says Dr. Michael Goller, CTO at Detego. “We can show how fashion retailers can positively influence buying decisions and how a digital fitting room or a chatbot help in the sales process, as well as witness how several global fashion brands have successfully adopted the Detego InStore Lean Edition.”
Detego, a provider of innovative software solutions for the retail industry, will be showcasing the latest digital in-store solutions on January 13-15 at NRF 2019. Returning as a co-exhibitor on the SAP booth (#3426), Detego will display their RFID-based inventory management software and latest AI applications for retailers.
Detego has been complementing the SAP offering in fashion retail by utilizing IoT technology and providing SAP systems with real time data on item level. Exhibiting at NRF will be the Detego InStore Lean Edition, a new mobile solution for retailers, offering faster and cheaper access to the benefits of digital connectivity via an RFID based system. This solution allows fashion retailers to quickly adopt a “smart” replenishment process and carry out “intelligent” stocktakes, by starting small and scaling across the entire store network. Offered as a SaaS (Software-as-a-Service) model with cloud hosting, retailers benefit from high inventory accuracy and consistent article availability at low cost. Detego’s software has already proven to be the most cost-effective and fastest to implement on the market with over 1500 stores running on Detego around the globe.
To complement their core product offering, Detego have also developed AI applications which benefit both retailers and consumers alike. Detego’s new digital assistant/chatbot can be used at any time on a customer’s smartphone to help provide more pertinent product information or recommendations, based on real-time data on actual availability and customer preferences. The built-in machine-learning and artificial intelligence (AI) capabilities adapt to the ever-changing dynamics of retail, which means that results get better and better over time.
Detego has also found opportunity within AI to help revolutionize the planogram. Since the manual maintenance of the planogram per store can be enormously time-consuming, Detego’s self-learning system adapts to possibly changing conditions and continuously optimises the individual planogram per store. “By optimising the sizing profile of individual items for each store and greatly simplifying the in-store refilling process, we provide retailers with tools that make it easier for them to plan and implement optimum product presentation and thereby help them to boost their sales.” Says Michael Goller, Detego CTO.
This webinar discusses intelligent planograms and how they enable an individual size distribution for fashion retail stores and make sure that products are available on the sales floor in the appropriate sizes.
It provide insights into how the self-learning system adapts to possibly changing conditions and continuously optimises the plan – with a direct impact on the store revenue.