AI & Fashion: An Unlikely yet Perfect Pair
At first glance, fashion – which is primarily driven by art, intuition and creativity- seems like an unlikely match for a clinical, analytical and logic-driven technology like Artificial Intelligence.
However, AI holds incredible promise for the fashion industry due to the massive data sets around historical style trends, sales patterns, market swings and customer preferences. For instance, did the global recession in 2009 impact people’s fashion sense and purchase behaviour? How about the Cold War? These are analysis that could yield interesting results and better yet, predict future behaviour and sales in response to global events.
Brands like H&M, Nike and Tommy Hilfiger are leading the way in leveraging AI-powered technologies to optimize the supply chain, enhance the in-store experience, and improve the overall customer experience. Merchandising teams are increasingly relying on data from stores, websites, mobile apps and loyalty programs to tweak designs and layer it with insights from social media, customer reviews and fashion forums to refresh the entire stock every few weeks.
According to Mckinsey, while AI in fashion is yet to hit critical mass it’s on its way there and likely to impact everything from design to manufacturing to customer engagement.
In fact, the implications for AI in Fashion is so much that even tech giants like Google & Facebook are recognizing the massive opportunity. Facebook recently launched ‘Fashion++, an AI-powered fashion companion that helps you tweak your look. And Google teamed up with Versace to reinvent the famed JLo dress.
AI-driven Innovations in Fashion Retail
With annual spending in AI expected to hit $7.3 billion by 2022, it’s no surprise that AI is set to become an integral part of the apparel industry. Here are top impact areas that AI will likely have on the fashion industry :
Smarter Demand Forecasting & Inventory Management
In the last 20 years, fast-fashion brands like Zara and H&M disrupted traditional apparel market by shortening the design-to-store time from a few months to a few weeks and trading quality for lower prices and fresh stocks. However, this model requisites a deep understanding of current fashion trends, customer preferences, and the ability to predict where the market is headed. If not, they run the risk of unwanted inventory, discount sales and profit loss.
According to Capgemini, using AI across procurement, supply chain, logistics, returns and in-store “pilferage” could generate $340 billion in cost savings across the industry by 2022
While demand forecasting techniques have been used to minimize the revenue loss, it was not always accurate as it solely relied on historical sales data and limited data sets. Machine Learning and AI technologies can incorporate learning from multiple sources and compute millions of data points to reduce demand forecasting errors by up to 50%.
The challenge so far in automating apparel manufacturing has been the inability of the robot to handle limp and flexible fabrics. However, Sewbo Inc., a Seattle-based startup found a fix by stiffening the fabric, thereby rendering it easier for the robots to handle the clothes. The startup recently launched its first robotically-sewn garment, which was crafted by a generic robotic system that was taught by Machine Learning algorithm on how to use a sewing machine. When coupled with accurate demand forecasting techniques, automated manufacturing has the potential to further shorten the design-store lifecycle down to a few hours!
Conversational Virtual Assistants
One of the major concerns for fashion brands is helping customers choose the right fit and style without having to sift through thousands of products. Enter chatbots and digital stylists – customers can now chat/converse with these virtual assistants for getting style tips, finding the perfect dress for an occasion, getting the right fit/fabric based on their personal preferences or choosing a preferred delivery method. Expect this trend is set to take off, considering the rapid growth of smartphone and app adoption where the smaller real estate necessitates a more intuitive and faster product discovery.
Bombsheller, another Seattle-based apparel brand has started on-demand designing and manufacturing of clothes to a market of just one person. The company relies on a community of artists to submit designs using 3D modeling tools that create a photorealistic picture of how the finished product would look. The brand uses powerful software borrowed from video game engines to render the exact product as it will look even before a single stitch has been sewn. These ‘virtual’ leggings get uploaded to brand’s online catalogue. Once a customer hits “Buy,” the printer inks on the graphic design, and a seamstress cuts and sews the leggings. The finished product is then shipped out the next day. What makes Bombsheller unique is that they don’t spend a dime until a pair of leggings is sold. That translates to no dead inventory, no guessing market trends, no warehouse clearance and no loss of profits.
In-Store Analytics & Engagement
Using deep learning, Natural Language Processing and in-store analytics, the Smart Stores of the future will offer superior in-store experience by borrowing the best of personalization, customer engagement and CX practises from their online counterparts. Smart cameras and people counters powered by learning algorithms will offer accurate store insights about visitor demography, persona, sizing, product preferences, and online behaviour to help retailers stitch together a rich customer profile and hyperpersonalize the entire store experience. Several retail brands are experimenting with algorithms that predicts the most valuable customers who are likely to make repeat purchases based on previous in-store behaviour, loyalty earn/redemption rates and number of store visits. Other in-store applications of AI include identifying customer’s current fashion sense to make personalized recommendations , cashierless stores, monitoring shelf-life and freshness of perishables and interactive displays.
Quality Control & Counterfeit Screening
The Hong Kong Polytechnic University (PolyU) recently developed a smart fabric defect detection system, called “WiseEye”, which uses advanced AI and Deep Learning algorithms to help manufacturers instantly detect defects and anomalies in the production line. The system has been proven to reduce the chance of producing defective fabric by 90%, thus significantly reducing loss and wastage in the production.
Several brands are also experimenting with Machine Learning algorithms to find and filter fakes and counterfeit products. This technology is also used by customs and law enforcement officers to verify the validity of premium products like sunglasses and purses which are frequently counterfeited
Another trend that has been picking up steam of late is visual commerce. It essentially allows customers to take pictures of clothing they like or styles they want to imitate and find the exact items for sale. Additionally, AI-enabled shopping apps allow customers to take screenshots of clothes they see online, identify shoppable apparels and accessories in that photo, and then find the same outfit and shop for similar styles. For instance, if you have a specific patterned dress in your mind that your friend had worn at an event, AI can enable you to locate it. You can click a photo of your friend, upload it on a fashion site, and the AI algorithms will do the rest.
Fashion Brands Spearheading Artificial Intelligence Adoption
Nike has been the frontrunner in adopting technology to keep up with the changing times and stay ahead of the competition. In the last 5 years, the sports apparel giant spend millions on an acquisition spree of digital media, Ecommerce and analytics startups like Celect, Zodiac and Virgin MEGA to transform itself into a tech and data company that also sells fashion and apparel. When visitors walk into Nike Inc flagship store in New York and log into the app, the brand knows who they are, their shoe/apparel sizes, what sports they play and their favourite colours. Nike has reaped the benefits of its technology investments by reducing product defects, returns, shorter lead times, and most importantly, higher customer engagement and brand loyalty. The brand recently launched an app – Nike Fit – that offers “hyper-accurate” sizing recommendations for its shoes by scanning your feet with a smartphone camera.
H&M was one of the many brands whose sales took a severe wallop due to the retail apocalypse. The brand is now leveraging AI and Machine Learning to claw its way out of the slump and transform its business by spotting upcoming fashion trends, reduce unsold stock, optimize logistics and improve sales. The world’s second largest fashion group has started full-scale implementation of several pilot projects that were aimed at using data to match supply and demand as closely as possible. H&M is also harvesting data from loyalty cards and sales receipt to customize merchandise for an individual store. The brand recently teamed up with Google to create ‘Coded Couture’ , a cool app that creates a personalized design for every individual by analyzing their activities and interests over a period of one week.
Tommy Hilfiger partnered with IBM and Fashion Institute of Technology to help designers create new clothing line by analyzing customer sentiment around the brand’s apparel and imagery. The project was aimed at identifying key trends in silhouettes, cuts, patterns and styles that evoked positive customer sentiments by analyzing 15,000 images of Tommy Hilfiger products, 600,000 runway images that were publicly available and 100,000 patterns. Going forward, the brand plans to augment its design process with AI-powered insights to alleviates the pressure on the designer around creating a mass appealing creation.
In 2018, the UK-based online retailer developed an AI system to help customers find the right size of garment when shopping online. The smart fitting tool personalizes sizing suggestions based on multiple data sets like customer’s previous purchases and returns, as well as an optional set of questions based on height, weight and fit preferences. The online retailer rolled out the Fit Assistant across 200 markets and is available across ASOS collections, exclusive labels and fashion favourites. This year, the brand partnered with an AR startup to launch an augmented reality catwalk for users of its fashion app. The technology allows customers to view models as if they are walking in front of them by simply pointing their smartphone camera at any suitable flat surface and clicking the ‘AR’ button on the app.
Stitch Fix is a San Francisco-based fashion startup with a unique sales model. It combines the expertise of personal stylists with insights of an artificial intelligence system to analyze data on style trends, body measurements, customer feedback and preferences to deliver personalized apparel right to their customer’s doorsteps on a regular basis. Once the delivery is done, the customer can choose to keep all of the products or return what they don’t like or need. This input is fed into the company’s AI-powered data vaults to make the algorithms even better at determining the preferred style for each person and current trends. The brand also uses data to create its own designs known as Hybrid Designs. The system works by disassembling attributes of style such as color, arm length and neckline, etc. It then analyzes the feedback and customer sentiments available for each of these attributes and mutates them slightly to create new designs to share with human stylists.
While AI has proven its effectiveness in improving all aspects of fashion retailing, it’s impossible to remove the human factor from the equation. When they work together, the results are supercharged productivity, better customer engagement and higher profits. This trend clearly indicates the evolution of AI as an Augmented Intelligence rather than an Artificial one.
What is a Channel Loyalty Program?
A channel loyalty program is a critical sales pillars and a crucial element in the growth strategy of your business. Channel loyalty is focussed on transforming the relationship between a brand and its trade partners from that of a purely transactional one to a long term, emotional connect. This is increasingly becoming critical for a brand to succeed in a competitive market.
The most important factors in fostering channel loyalty are implementing value-driven loyalty schemes, transparent two-way communication, personalized incentives and driving emotional connects through constant engagement and experiences.
Why Brands Need a Channel Loyalty Program?
A B2B loyalty program is a powerful way to create loyal, long-lasting relationships with your stockists, dealers, influencers, resellers, and retail partners. They are critical for brands to survive in a highly competitive, globalized economy where competition is intense and multiple brands are battling it out for the attention of distributors, influencers and retail partners.
A well-strategized channel partner loyalty program can positively influence the willingness, and motivation of your channel partners to recommend and sell your products and services. It can also unearth new sales and revenue opportunities as well as function as a recruitment channel for onboarding new partners and increasing your overall distribution network
More than 80% of distributors confirmed that the opportunity to get rewards from the supplier is a critical factor in their purchase decisions.
Much like its B2C counterpart, the primary goal of a B2B loyalty program should be to elevate the relationship between a brand and its partner from that of a purely transactional one to one that of a long-term, emotional one. This can help your brand to create reliable, long-term revenue and more importantly, increase your brand mindshare amongst partners as well as your end consumer.
Why Partner Loyalty Programs Fail
Gaining the loyalty of your channel partner can be tricky but it’s not impossible. The biggest mistake most brands make with their channel loyalty programs is focussing heavily on transactional and monetary rewards.
In fact, research shows that companies that implement non-cash reward and recognition programs for their channel partners record annual revenue increases averaging 10% compared to other companies which average 3%.
Here are some other pitfalls you should avoid while executing your channel loyalty program :
Lack of commitment
For a loyalty program to work, it must be well planned, have stakeholder buy-in and promoted extensively. It’s recommended to have a dedicated program manager who oversees the loyalty program and reports the sales and revenue impact to the senior management.
Lack of personalization
If your brands operate a complex supply chain featuring distributors, dealers, influencers, sub-dealers, retailers etc, it’s important to customize the rewards and incentives for each segment. It is also important for brands to have clarity about the behaviour they want to encourage; it could be different for a distributor than a reseller, for instance.
Not setting measurable goals
It’s important to attach measurable goals like increase in sales, revenue or higher market share to your channel loyalty program to ensure its success. The goals should also be communicated to your sales and marketing team. Also, it’s important to get an understanding of your channel partner’s goals and motivations. This will help you to sync your own business goals with that of your partner’s and thereby create a symbiotic partnership.
Complex program structure
The success of your channel loyalty program depends on how seamlessly it can integrate into the routines of your partners. Ensure that the sign up is hassle free and you list out the rewards and other terms/conditions upfront. Also, ensure that points and rewards are attainable; making either of them too difficult to earn will disengage partners from the process.
Not leveraging data
A channel loyalty program with advanced analytical capabilities can help you track the overall health and status of your loyalty program. To ensure the success of a program, it’s imperative to constantly check the participation and redemption rates along with its sales pipeline contribution. An added benefit of tracking the progress of your channel loyalty program is it can uncover potential upsell/crosell opportunities for a specific region.
Launch it and forget it approach
Most brands treat their loyalty programs as an autonomous entity that can function on its own once it’s launched. Unfortunately, this leads to the decline and eventual failure of a program. The primary reason is that there are several brands vying for the attention of the same partner and your loyalty program needs to be exceptionally good for it to stand out.
Strategies for Effective Channel Loyalty Program
While the actual strategy will vary depending on the target audience goals and aspiration of your specific audience (distributor, wholesaler, retailer etc), here are some key things which you will need to incorporate into your loyalty program to ensure its success.
Understand Partner Goals
This is the first step in implementing a channel loyalty program and should form the core of your strategy. Make sure you segment your audience based on their goals and offer them unique, personalized goals and rewards. If your loyalty program doesn’t sync with your partners’ strategic goals, adoption and redemption will suffer
Easy Reward Redemption via Ecommerce website/mobile app
Whether it’s a gift card, a holiday or an appliance, make it easy for your partners to redeem their rewards, if not your program will slowly lose traction. The ideal way to do this is to setup a dedicated B2B ecommerce app or website, where your partners can login and choose from multiple incentives in a single platform.
Setup Member Events
Constant partner engagement is critical for the success of your channel loyalty program. Try to organize localized events and meet/greet sessions where you can communicate with individual partners. This is also a great way to build a long lasting relationship with your partners and ensure lower marketing costs.
Personalization & Segmentation
No two partner’s motivations and goals are alike, therefore it’s important to offer highly personalized goals and incentives. Make sure you set clear rule structures for each partner centered around their behaviour that consistently produces business results. The personalization aspect should also flow into your communication strategy. For instance, language barriers and other geo specific cultural nuances need to be taken into consideration for effective reach and engagement.
Tier-based incentives have been really successful in the B2C context and works very well for partners as well. This structure essentially aligns with the aspirational mindset that is core to human behaviour. Start from a base tier for signups and offer higher incentives as the partner moves up the tier based on their sales and purchase volumes.
Gamification concepts like Leaderboards, Spinwheels, Engagement Trackers and sporting event (IPL , World Cup) related activities can help boost engagement levels of your loyalty program. For gamification to be successful, it must sync with the user journey, your business goals, and communicated clearly to the partners through multiple channels like emailers, app notifications, SMS and sales visits.
Benefits of Channel Loyalty Program
Boost Sales & Reach
A channel loyalty program directly impacts the sales and revenue growth of your brand by motivating influencers and retail partners to sell more of your products.
Gain Channel Insights
Data from your channel loyalty program can offer you insights into product sales and customer preferences in specific geos. These insights can be leveraged to gather best practises and optimize your inventory and supply chain in the long run.
Real-time Partner Engagement
Advanced channel loyalty program software lets you engage with your partners in real-time. This can help boost brand mindshare, loyalty and ultimately sales and conversions.
Efficient Channel Management
Get insights on the product preferences and sales across multiple channels and leverage it to identify bottlenecks, crosell/upsell opportunities and potential new markets.
Lower Acquisition Costs
By encouraging existing partners to refer to new partners, an efficient channel loyalty program can significantly reduce your cost of acquisition.
Long-term, Profitable Relationships
This is the ultimate goal of a channel partner loyalty program and can have a major impact on your sales and brand loyalty.
Start increasing sales & partner engagement with Capillary’s Channel Loyalty Solutions
Over the last few years, rapid digitization has made a significant impact on the Saudi consumer’s lifestyle. Today, a whopping 88% of Saudis use the internet at least once a day and 59% of the population are now present in at least one social media platform. While the growth in digital usage in Saudi Arabia wasn’t really a surprise, what has befuddled even the experts is the absolutely staggering growth rate.
While the level of internet penetration and smartphone usage in Saudi Arabia is comparable to other countries in the GCC, the country is an outlier when it comes to consumer demographic. With a GDP per capita of USD 20,028 and a median age of 29 years, the typical Saudi consumer is wealthy and extremely tech-savvy. It’s also the sole country in the GCC where the locals account for 72% of the population.
This young, tech-savvy and wealthy population in Saudi Arabia serves as the perfect substrate for eCommerce growth, and the number of online shoppers in the region is expected to reach 17.1 million by 2020.
Smartphones are increasingly becoming the preferred device for online shopping with 69% of Saudis preferring it over a desktop. However, offline promotions and engagement remain a critical aspect of the purchase journey with 35% of Saudi consumers preferring to research about the product through offline channels prior to purchase.
From a business perspective, the region is not without its share of challenges. Socio-economic shifts in the region like fall in oil prices, the introduction of Value Added Tax, and an exodus of expats have had a negative impact on consumer sentiment in the last few years. Amongst these, the biggest impact was from the introduction of 5% VAT. While a relatively low rate, it resulted in a significant shift in consumer behaviour. The average Saudi consumer became more cost-conscious than ever before and the trend is expected to continue at least for some more time. However, there are positive signals that the situation is being reversed.
Customer confidence levels in the Kingdom for the month of June 2019 was 62.1; placing it at the top 3 next to China and India (the global average is 43.3). The economic recovery, supported by higher oil prices and other internal social changes like expanded women’s rights, are further expected to have a positive impact in shaping consumption and market dynamics in the coming years. Brands that can crack economies of scale, and provide value-add or greater convenience, will reap the rewards.
Key Takeaways for Brands :
1. Localization is critical to survive and grow in the region
2. There is a growing interest for western apparels, both for online and offline shopping
3. Consumer spending has slowed; brands will have to innovate using personalized promotions and provide value-add to stay ahead
4. The new breed of GCC consumer expects a highly personalized experience.
5. The shift towards price-consciousness is expected to lower brand loyalty among consumers
6. Ecommerce will continue to grow at a steady pace; especially from mobile devices
7. Offline customer engagement will be a critical factor in influencing purchase decisions and brand loyalty
To know more about the event, register here: https://www.capillarytech.com/know-how/stayready/riyadh/2019/sep
Lessons from the Retail Apocalypse
The traditional way of ‘stock up and wait for a sale’ has already spelt the doom for thousands of brick and mortar retailers across the globe. However, the retail apocalypse has somehow propagated the false notion that consumers have all of a sudden stopped shopping from retail stores and malls.
On the contrary, 61% of consumers would rather shop with brands with a physical store than with pure-play ecommerce brands, according to Google. Also, nearly 80% of shoppers go in-store when there is an item they need or want immediately.
The likely hypothesis for this disconnected demand and supply trend is that customers are visiting the stores but leaving without making a purchase. How can retailers fix this?
A good north star would be to analyse brands like Nordstrom, Walmart, and Ulta who were not only unaffected by the doom and despair but in fact, thriving in this turbulent retail landscape. They achieved this reimagining their retail experience through a combination of experiential shopping, new-age technologies like VR and AR and loyalty program restructuring.
While the implementations and solutions might have been different, the common thread that ties these brands together is the fact that they no longer treat customers as a one-dimensional, passive participant who wants to mindlessly scour aisles of inventory.
Instead, they rely on a deeper understanding of customers to anticipate their needs and create hyper-personalised micro-moments throughout the purchase journey.
Why In-Store Engagement is Critical for Retail Success
Over the last few years, customer experience has deftly evolved from a ‘good-too-have’ to ‘critical-and-indispensable’ for retail brands. According to Garnter, over 80% of organizations expect to compete mainly based on CX in 2019. The likely reason for this sudden obsession with CX is most likely a commodified market, where the lines between competing brands and products get increasingly blurred. Therefore, the only way your brand can stand out is through stellar experiences. And your customer engagement strategy forms the crux of your overall Customer Experience.
According to Qualtrics, brands that lead in CX, outperform laggards on the S&P 500 index by nearly 80%. Moreover, these brands enjoy a higher wallet share, repeat purchases and positive word of mouth.
‘A well-executed in-store engagement strategy creates a win-win situation for the brand as well as the customer.’
From a brand perspective, combining customer data and analytics with friendly, face-to-face staff interaction reveals a whole universe of opportunities for maximizing Average Order Value (AOV), brand loyalty and repeat sales. From a customer perspective, a personalized interaction flips the experience from that of a purely transactional one to one that is emotionally-fulfilling and memorable.
While online customer engagement strategies have been debated and discussed to death, not much attention has gone the other way – the In-store engagement. So here are the major ways you can overhaul/enhance your in-store engagement:
Best Practises for Boosting In-Store Engagement
- Leverage Real-time Promotions and Deals
These days, most retailers offer some kind of promotional voucher that goes: get a discount voucher for transaction value above X amount. Unsurprisingly, these types of generic and one-size-fits-all promotions rarely get any traction. Instead, what if you could analyze and connect the dots between the customer’s in-store behaviour, previous purchases, and online browsing patterns to generate a dynamic voucher that’s personalized for them, right before checkout. The rate of redemption drastically increases simply because you are offering them something exclusive and value-driven.
- Empower Your Store Staff with 360-degree Customer View
A personable and friendly staff interaction can definitely improve customer experience but it might not go a long way in increasing store sales. Today’s connected customer like it when retailers are going beyond the usual ‘hellos’ and ‘have a great days’ and start paying personalized attention to them and their shopping habits. It can be as simple as reminding them that their anniversary is coming up next week or remembering their preference for slim fit trousers. Technology can play a key role in powering these personalized interactions by offering employees in-depth customer information like demographics, past purchases, online and in-store browsing behaviour, special occasions, etc. Advanced retail analytics can even empower store staff to offer predictive recommendations by comparing purchase patterns across customers of similar personas.
- Avoid Loss of Sale due to Stockouts
‘We don’t have that in stock’ is a much-dreaded phrase in retail circles and for a good reason: it has a collective negative impact on sales, customer loyalty, and customer experience. While preventing stock-outs seem like a fairly simple task to the outside world, you as a retailer, understand the precarious and delicate balancing nature of ‘having enough stock’ and ‘over-stocking’. Fortunately, armed with the right technology you can now know precisely when to stock up and even better – offer customers the option of buying exactly what they are looking for even when you don’t have it in stock at the store.
Here are some of the ways you can reduce stock-outs :
- AI-Powered Inventory and Order Management: Inventory systems powered by Artificial Intelligence are often cited as the most effective way for retailers to reduce stock-outs. These intelligent systems maintain a centralized inventory and automatically predicts the fastest and most cost-effective means of fulfillment. Moreover, they also offer predictive insights on stock replenishment by analyzing multiple factors like store traffic trends, customer demographics, weather, historical data etc.
- Endless Aisle Interface: Endless Aisle refers to the concept of using enabling customers in your stores to virtually browse or order a wide range of products that are either out of stock or not sold in-store and have them shipped to the store or their home. For the retailer, the endless aisle provides the ability to offer a much broader product assortment without the cost of having it put on shelves or stored in the store. Several retailers have already synced their Endless Aisle interface with Magic Mirrors and Virtual Trial rooms to further elevate the shopping experience. An important factor to consider while implementing an Endless Aisle interface is store staff training. If the associates aren’t well versed with the interface or doesn’t understand communicate the benefit to the customer, it has little chance of succeeding.
- RFID (Radio Frequency Identification): RFID chips enables retailers to track and count merchandise with a hand-held scanner. This helps in efficient inventory management through a combination of speed, cost-effectiveness and minimal inaccuracies due to human error.
- In-store Analytics: Sophisticated footfall counters let you know your customers better and measure peak store traffic hours. These insights will help you schedule your store staff efficiently thereby minimizing the chance that shelves are not being restocked due to lack of resources. For instance, an AI-Powered Footfall Counter can give you insights like female customers in the 25-29 age group are more likely to visit your store from 8 PM to 9 PM on Fridays.
The Way Ahead
Regardless of which engagement solution you choose to implement, personalization should form the crux of your in-store engagement strategy. Imagine it as a layer that permeates across all in-store customer interactions, right from greeting, to product recommendations, to after-sales interactions.
Implementing an in-store personalization strategy essentially involves three steps
- Acquiring customer data like age, gender, purchase patterns, browsing history, etc
- Analyzing the data to determine customer preferences
- Serving relevant, personalized shopping experience based on the data
As emerging technologies like ML and AI mature, they will add a new dimension and depth to retail personalization by offering powerful capabilities like voice, facial and sentiment analysis to gauge emotional states and predict behavioural patterns of customers.
The Personalization Gap in Offline Retail
At a time when Ecommerce is so competitive thanks to smartphone and internet penetration, the yesteryear model of retail, brick and mortar, is threatened. When products are available at the customer’s convenience at discounted rates, he is more likely to use the service as opposed to going to a store and buying it. Ecommerce companies typically encourage and enable consumerism through a combination of convenience and value. However, where pure-play online brands have a clear edge over offline stores is their ability to personalize customer interactions across the purchase cycle – from acquisition to fulfillment. On the other hand, offline stores have a tendency to provide a one-size-fits-all experience for the customers who walk in through their doors. If a brick and mortar store is to keep up with ecommerce companies and their hyper-personalization capabilities, the stores will need to start collecting, analysing and leveraging customer data – In-store Personalization is the way to go!
So why is In-store personalization is so vital for a retail brand?
According to a report by Medallion Retail, 46% of shoppers will buy more from a retailer that provides a customer with a personalized shopping experience. This is another sure-fire way to convert a first-time customer into a returning customer and win his loyalty. Moreover, customers who shop online are used to this level of personalisation and they expect a similar experience in a real-world situation as well.
A one-to-one retail experience is essential for in-store personalization. The key is to know enough about your customer’s information in the three tenses – past, present, and future.
- Past: It’s important to know the customer’s purchase history – what products they’ve searched, interacted with. This is so that the business can gain a better understanding of the consumer preferences in order to deliver a tailor-made in-store experience for him.
- Present: Which zones of the store is the customer visiting? Which areas are does he/she stop at? What items hold his/her attention? Observing a customer’s behaviour while he is inside the store can help you gain valuable insights about how to serve him better based on his tastes.
- Future: What are the products that the store stocks that can possibly interest a customer who is going to come in? What offers can you give the customer to entice them to make a purchase? What reward can you offer to a one-time customer to convert him/her into a returning customer? These are some of the questions that a store owner must be cognizant of when it comes to personalizing his in-store experience for the customers. In this manner, in-store personalization becomes a key part of the omnichannel strategy and omnichannel retailers will need to make the connection between a customer’s online experience and their offline journey through the brick and mortar store.
The Offline Retail Advantage
The Medallion Retail report also points out an interesting insight – that customers prefer to be acknowledged in-store rather than via digital channels. While ecommerce companies may have access to a vast database of user information, it is entirely possible that a user can get irked by an ad that feels too personal or invasive. In this way, in-store personalization is a good way to make a human connection with your customer and make him/her feel they are cared for. It is important to train your staff in such a way that they are well equipped to handle your customers’ needs. In this way, in-store personalization sets the stage for an offline business’ omni-channel marketing success. It is the final step that works in collaboration with other steps such as loyalty programmes and online shopping options in order to make the omnichannel marketing program a successful one.
Benefits of in-store personalization in retail
From a brand’s point of view, in-store personalization can bring tangible results to a business in terms of higher customer engagement and conversion. A report by Infosys states that 59% of consumers who have experienced personalization believe it has a noticeable influence on purchasing decisions. The same report also states that a whopping majority of 92% of the consumers who are in a store, look to discover new products and are open to making impulse purchases even if it’s not on the agenda. In fact, consumers are three times more likely to make an impulse buy in-store rather than while shopping online.
Personalized engagements with precision targeting
Access to data such as email IDs and brand preferences enable a business to tailor marketing efforts to suit their customers tastes. Beyond the marketing messages themselves, knowing what a consumer prefers enables a marketer to speak in the same language as the consumer – making the marketing exchange conversational and more personalized.
It is much more economical for a business to retain an existing customer than to acquire a new one. Taking the efforts to personalize the retail experience for a customer increases the chances of an incidental customer becoming a returning customer who is eventually loyal to your business and your brand. The end goal of a marketer is customer retention. Personalization is a powerful way of building upon the relationship that a customer has with a business and it contributes towards retention efforts in the long run.
Increase digital mindshare with relevant content
Knowing a customer’s preferences enables a brand to create content that resonates with its customers. It could be content for email newsletters, or content for an ad copy or a simple post purchase ‘Thank you’ card. For example, if a marketer has the knowledge that their target audience likes science fiction movies, then it becomes easier for him to create content in such a way that the target audience finds it relatable. Once he does, you have grabbed your customer’s attention and increase the chance that he/she will share it with her friends/followers in the digital universe. Smart offline retailers are also using these digital data to further personalize the in-store experience for their customers.
Sync offline and online customer personas
Customers are more likely to associate with a brand and turn into returning customers if they interact with the brand both online and offline. 89% of buyers surveyed in the Infosys survey admitted that interacting with brands has some impact on their purchasing habits. Traditional retailers who are able to connect the online personas of their customers (eg:-across social media, website, and app) with in-store browsing behaviour will be in a position to offer hyper-personalized engagement across offline and online channels.
Make your Marketing More Impactful
We live in a world that’s densely populated with screens. In such a situation, there tends to be an information overload from brands who are trying to make their digital presence felt. When this is the case, brick and mortar retail stores have the advantage of getting the customer’s full and undivided attention in the store.
Implementing In-store Personalization
- The first step in implementing in-store personalization is to know your advantage over your competition.
The major advantages that brick and mortar stores enjoy over their online peers are : tactile experience, human interaction and higher Average Order Values (AOV). To stay ahead and be relevant, retail stores will need to understand how to leverage each of these strengths. Of these, higher Order Values is something that can be greatly enhanced through personalized recommendations based on the customer’s previous purchases and in-store behaviour data collected through movement, dwell time and voice/sentiment analysis.
- The second step is to understand your audiences’ preferences.
Once you have a pulse on your customer’s preferences, tastes, and requirements, then you will be better placed to accurately deliver what they require. Knowing exactly what your customers need also puts you in a position to deliver marketing messages of utility that they do not ignore. A retailer can also extensively use technology in order to create a personalised experience for the customer. The store can equip itself with augmented reality technology so that the customers can try on items in real time.
A store can also use an AI-powered footfall counter in order to increase store conversion and sales. These in-store vision tools help a business unlock powerful retail insights about customer demographics, store conversion ratio and the impact of marketing campaigns. Capillary’s VisitorMetrix is a 95% accurate people counter that’s equipped with an intelligent people tracking system to capture age and gender demographic data in real-time. This data can further be used to personalize the in-store experience for the customer. Ecommerce platforms have access to these insights and therefore deliver tailor-made ads but with VisitorMetrix, the insights it gives you can empower your business to optimise store inventory and predict customer preferences in order to maximize sales and conversions.
- The final step is to optimize staff deployment and empower them with customer insights
It is important that your sales staff is equipped to create a personalized in-store experience for your customers. VisitorMetrix can help you optimize staff strength and set conversion goals based on store footfall data. In-depth footfall analytics can also be used to schedule staff breaks and store maintenance at the ideal time in order to prevent losses and loss in sales.
Examples of In-store Personalization Done Right
Starbucks has a robust loyalty program that is personalized according to the users preferences and serves his needs. The loyalty program also comes with perks such as personalized offers for customers using the app and a free beverage on their birthday. Starbucks has access to customer data thanks to its loyalty app. People are comfortable sharing their data via the app as they are aware of the kind of rewards it can yield. It’s a win-win situation for the business and for the customers.
Even though Macy’s is a department store that was first opened in 1858, they have quickly adapted their retail strategy to suit the digital age. Macy’s extensively uses big data to offer a smarter customer experience. They analyse multiple data points such as stock levels and price promotions. The analysis of these data points are further combined with stock keeping unit data for particular products at particular locations. Macy’s also closely studies customer data to ascertain which products sell well at which store location. They also study how frequently a customer visits a store and what style they purchase. This ensures that the products that they have in their stores suit the customer’s buying habits in each location. Using this data, Macy’s even offers incentives at the point of sale in addition to loyalty points and promotions. The data they collect also enable them to send directly targeted mail to their customers, hence enabling them to boost conversions.
When you are a Target customer, you will be assigned a guest ID number after you interact with the brand for the first time. That ID is used to store data that is related to customer demographics. This ranges from ethnicity to job history and it is used to track buying behaviour. Target realized that their consumer’s buying patterns remained largely unchanged unless a big event happens in their life – such as pregnancy. Target found that it could ascertain which of its customers were in their early stages of expecting a baby when they purchased things that they never previously considered – such as diaper bags or cocoa butter lotion. Once Target could ascertain those behaviours, it enabled them to provide their customers with special deals on baby-related items that were personalized and tailor-made to suit their requirements.
At a time when mall-based retailers are struggling to maintain numbers due to reduced footfalls, Abercrombie & Fitch is investing in their digital capabilities in order to ensure that they capture the millennial market. Abercrombie & Fitch are well known for their personalized email communications and their loyalty program. The retailer personalizes e-mails communications with the help of in-store and online data about styles and brands that the customers prefer and use. The retailer also encourages shoppers to use their mobile app and rewards their loyalty with loyalty points and other benefits.
Future of In-store Personalization
The rise of Machine Learning and Big Data is expected to have major implications in the retail space, especially with regards to personalization and the in-store experience. Retailers will likely be able to enhance in-store personalization through advanced computer vision to see the profile of the customers who walk in and leverage voice analysis, emotional state and facial signals to predict the likelihood of a sale and probability of a repeat visit.
The Double Whammy Staring at Marketers
There was a time when only brands with massive financial clout could afford to run major marketing campaigns, simply because Above the Line (ATL) marketing is resource intensive. All of that changed with the digital explosion; now your neighbourhood mom and pop store could launch a Facebook or Adword campaign for as low as $50.
This resulted in an exponential rise in competition in the digital space for mindshare and customer loyalty. Unfortunately, the digital influx also pummelled consumer attention span to a meagre 8 seconds (a sharp decline from 12 seconds in 2000). As a result, today’s marketers find themselves in an intensely competitive universe where thousands of brands are battling it out for the ever-shrinking attention span of the consumer.
The Rise of Digital Marketing
In the new digital universe, consumer attention and loyalty soon became an invaluable and coveted resource. And brands started pouring millions of marketing dollars to capture consumer mindshare. For a while, this worked, but the human psyche is incredibly good at filtering out irrelevant information and there started a growing distaste for ads. During the period from 2015 to 2019, the number of Americans using adblockers grew from 15% to 26%.
Marketing 2: 0: Engaging the Connected & Empowered Consumer
Smart marketers soon realized that blasting consumers with a barrage of generic, irrelevant communication is hurting them twofold: it quickly drains precious marketing budget and also spreads the sentiment that the brand is apathetic to consumer needs. To gain mindshare in an attention-deficit consumer, marketers needed to make personalized communications
A key pillar within this reformed marketing philosophy is personalization because it sets the stage and gives a contextual framework for a relevant and value-driven engagement.
The Holy Grail for Marketers: Hyperpersonalization at Scale
In its simplest sense, personalization is a targeted approach to customer engagement that delivers tailored, useful and relevant communication to every customer. For smaller businesses with small to medium customer sets, the personalization efforts are fairly simple and achievable with a good analyst team. But it gets increasingly complex and a herculean task at enterprise levels of data and consumer sets. In fact, less than 10% of top retailers say that are good at effective personalization.
When done well, personalization accelerates sales and business growth for brands while increasing overall customer satisfaction levels. Personalization improves conversion rates by a whopping 70% and it impacts ripples across the entire consumer lifecycle: from acquisition costs, engagement levels, average order values to repeat purchases.
The challenge to implementing personalization at scale basically boils down to two things: the pace of change in consumer behaviour and exponential rise in data volumes. Combine these two and you get rapidly changing data, constantly shifting customer segments and frequent changes in the type of insights needed from the data.
Smart Segmentation with AI: Powering the New Personalization Paradigm
Since smart segmentation serves as the primer for personalization, it’s vital to create relevant and accurate segments before embarking on your engagement strategy.
The Need for Smart Segmentation
While basic segmentations like demographics and location can be done manually, creating complex personas and purchase patterns segments can be tedious and time-consuming. Moreover, these generic segmentations cannot be used for building advanced purchase propensities modelling.
For successful smart segmentation, brands will need to layer basic segmentation attributes with ‘behavioural aspects’ of a customer. These include explicit behavioural aspects like purchase history, search history etc. and implicit behaviour aspects like dwell time on a specific product page, heatmaps and other storefront interactions. Moreover, these behaviour segments need to be dynamic and adaptive based on evolving customer needs.
This is where emerging technologies like AI and ML can help brands optimize their data and resources to achieve their business goals.
AI-Powered Adaptive Segmentation
The true power of Artificial Intelligence lies in its ability to find complex and disparate correlations which are almost impossible to uncover through manual intervention.
The need of the hour for brands is to value-driven experiences at every stage in the customer journey. However, multiple data sources and non-linear customer journeys have made it difficult for brands to create a seamless customer experience. Adaptive segmentation is a great way to create centralized segmentation based on multiple factors like demographic data, behaviour metrics and time.
Adaptive segmentation is powered by an intelligent algorithm that constantly ‘learns’ more about the customer every time he/she engages with the brand – whether on social media, email, website or in store. Adaptive segmentation also enables brands to optimize segments based on higher conversion probability rather than simple demographics metrics.
4 Step Process to Smart Segmentation
AI-based segmentation typically comprises of 4 stages :
- Pre-processing: The initial validation and cleaning of data sets. A ‘gold standard’ training set will need to be identified at this stage which will serve as a primer for future use cases.
- Modelling: This is where you identify the variables that make up your segmentation. These variables are then stacked based on the order of importance and applied to the ‘gold standard’ training set to help the machine understand the common properties for your segment. The popular segmentation attributes are :
- Traffic source (website, PPC, email campaigns, social media etc)
- New or returning customer
- Platform (mobile, desktop)
- Average Order Value
- Demographic, Likes and preferences
- Search Behaviour
- Past content interactions (product page, blog post)
- Evaluation: At this stage, a confusion matrix is used to identify previously incorrect contacts and also evaluate the accuracy of the model. If there are unbalanced data sets across the segments, a statistical coefficient is applied to account for class imbalance.
- Transformation: Output data is achieved and your customers are now smartly segmented in accordance with the ‘gold standard’ training set.
Benefits of AI-Powered Segmentation
- Eliminate human bias and stereotyping while segmenting
- Create an almost infinite number of segments and sub-segments
- Real-time updation of segments based on current behaviours and market trends
- Uncover hidden patterns and trends (that goes against prevailing assumptions)
- Highly scalability
- Higher engagement rates and ROI
Despite the overwhelming success rate of AI-powered segmentation in driving personalization, its adoption rate has been low with 55 percent of marketers admitting to not implementing it due to siloed data sources and in some cases, lack of sufficient data itself. To stay ahead of the digital revolution and be consumer-ready, it’s imperative for brands to create centralized, data pools using omnichannel analytics platforms and deliver a more personalized customer experience using AI-powered marketing solutions.
Sci-fi geeks previously mocked for their far-fetched ideas about robots taking over the world can today smugly say, ‘hah! Told you so. We are no longer at the cusp of an era of artificial intelligence; we are living in it. Our daily lives are peppered with gadgets that use voice recognition, search predictions, and facial recognition. From systems that forecast the weather to those that predict stock prices, to self-driving cars—we are breaking new frontiers so rapidly that we rarely stop to think, about the constant evolution of these machines as we use them.
Artificial intelligence, simply put, is the ability for a machine to learn. It does this by continually analyzing patterns from the data it collects. The greater the collection of data, the more accurate the machine can become at making predictions. Gartner, Inc, projects that over 25% of customer interactions will be managed with virtual customer assistants by 2020. Machine-learning technologies can go beyond the scope of humans by learning and understanding every individual customer, even in an audience of millions.
The evolution of AI
Artificial Intelligence truly began in the unleashed imagination of fiction writers. Although the rich pages of Isaac Asimov may come to mind, the technology started impacting the real-world only in the 1950s when Alan Turing contemplated the question, “Can machines think?” The Turing test was once considered to be the benchmark for Artificial Intelligence—to pass the test, a machine must deceive a questioner into believing it is a human.
In recent decades, AI research has seen unprecedented acceleration. From the year 2008 to 2012, it was growing at roughly 5 percent annually; but since then, it has boomed with a growth of more than 12 percent annually. Today, Europe still leads in AI research, but within the next four years, China is predicted to take over as the global pioneer in artificial intelligence.
A New Era
At the same time, trends have been changing in the world of business and retail. In 2018, e-commerce sales accounted for a whopping 11.9% of all retail sales worldwide. The number of global digital buyers is expected to rise to over 2.14 billion by 2021.
Since every marketing strategy boils down to understanding what consumers want, this puts AI at the forefront of the new era of commerce. Artificial Intelligence collects and organizes heaps of data that manual processing would take years to do (if at all). Based on this analysed data, it can make predictions that can save an organization vast amounts of time and money.
According to Juniper Research, AI chatbots could save businesses up to £6 billion per year across industries. But more importantly, it can give an organization greater precision while marketing, by knowing their customers better.
Artificial Intelligence in eCommerce
Here are some key ways that AI has made an entrance into the realm of e-commerce:
It might soon be the end of the line for contact forms, email and phone calls. More importantly, it’s a step in the right direction when it comes to delivering stellar customer experience. According to a report, 51% of customers never come back to a business after a bad experience. By kickstarting the ‘conversational commerce’ trend, Chatbots give e-commerce websites the ability to provide 24/7 customer support. They simulate conversations with customers and can execute tasks, automate order processing, and can also provide accurate answers to customers about product details, quantities and shipping terms.
According to a 2017 report by PwC, 34% of executives say the time they freed up using chatbots allowed them to focus on deep thinking and creating.
Chatbots increase response time and answer more than 80% of regular questions; freeing agents to carry out other tasks. Brands like Lyft, Alibaba, and Spotify are using chatbots to carry out various operations, such as booking a cab, finding the right music, and placing orders.
2) Image search
With AI, consumers can now search for products based on images they’ve come across. See an outfit you love? Simply take a picture and get matched to similar items on ecommerce websites.
Pinterest is already leveraging this technology by allowing its users to select any item from any photograph online and then throws up similar items through an image recognition software.
Since ecommerce sites process multiple financial transactions throughout the day, they are frequent targets for cyber attacks, fraud, and unauthorized access from hackers. Cybersecurity is a priority among top ecommerce stores that realise that cyber attacks can cost millions, and worse, damage their reputation as a trustworthy brand.
AI systems can detect irregular patterns, like spam and fraud, because of constantly processing data, and sound the alarm in real-time when it detects suspicious activity. For example, the cybersecurity company Prolexic monitors malicious cyber threats globally and analyzes DDoS attacks using their proprietary intelligent system.
Customer Relationship Management (CRM) have traditionally relied on employees to collect huge amounts of data in order to better serve clients. Today, several CRM software are capable of leveraging artificial intelligence to help in identifying trends, and predict which customers are most likely to buy products with impressive accuracy. This frees up sales teams to focus on their revenue goals.
5) Recommendation Engines
There are those who are familiar with recommendation engines, and then there are those who are shocked when their phone throws up ads for exactly what they needed. Is it a conspiracy?
eCommerce platforms powered by Machine learning algorithms are capable of analyzing customer behaviour from past searches, ‘like’ history, frequently bought products, and can recommend products for the user. Amazon, Facebook and Instagram are examples everyone is familiar with, frequently giving us recommended products and ads based on our history. Newer ecommerce store builders are capable of digesting massive amounts of customer data to generate highly personalized and contextual recommendations.
AI has the potential to spike sales and productivity like never before. Today, more than 75 percent of companies are using AI to power their decision-making processes and accelerate business development (as reported by Capgemini, 2017).
Perhaps the world is changing at a pace where we need to hold onto our seats, but Artificial Intelligence is more than a buzzword. It is a field of research that is unlikely to disappear anytime soon and businesses will do well to jump on the bandwagon at the earliest.
Being an ecommerce marketplace seller is not an easy job. You are sandwiched between marketplace regulations and ever-increasing customer expectations for superior buying and fulfilment experience. Thankfully, many of the roadblocks and issues plaguing ecommerce marketplace inventory management can be easily resolved by establishing a solid inventory strategy from day one.
Understanding Marketplace Inventory Management
Inventory Management is the lifeline for marketplace sellers since the success of the business hinges on delivering the right order to the right customer at the right time.
Here are some of the basics of inventory management you should be aware of before starting your journey as an ecommerce marketplace seller :
- Organize Your SKUs – Stock Keeping Units (SKUs) are essentially unique identifiers assigned to each of your product to streamline the ordering and logistics process. SKUs can be customized as per your wish but they are typically categorized based on product, category, popularity etc. The first step in establishing an effective inventory management system is to have a simple and well-structured SKU system in place.
- Focus on Product Types – In order to ensure a seamless workflow and minimize time wastage, you should have a thorough knowledge about the product type you are selling on marketplaces. Here are the major product types that ecommerce marketplace sellers usually deal with :
– Item : An individual product or an item that doesn’t require any special storage or shipping considerations
– Case pack : A group of usually similar items clubbed together. They might require special attention during storage
– Assembly : These are items that require assembling multiple parts scattered across your warehouse before shipping. Based on your sales forecast, you will need to organize them based on individual parts or end products
– Family : Similar products that have variations on colour and size. These will also need to be grouped based on your sales and demand forecast
- Get Serious About Demand Forecasting – Use historical data and other demand forecasting techniques to prevent negative sales impact like stock outs. Also, pay close attention to your sales analytics to understand the rise and fall in demand for your product. Make sure to take into account seasonal variations and other customer behaviours that will impact the sales of your product.
- Have a Backup Plan – Even the most experienced ecommerce sellers will not be able to predict product demand 100% accurately every time. At some point or the other, a random occurrence or event will disrupt even the most the most watertight strategy. Smart sellers should thus always have a backup plan in terms of surplus inventory of bestselling products or a reserve supplier.
- Ensure Data Accuracy – Ultimately, inventory management is a case of numbers and data and even minute discrepancies can have a severe impact on your business. Some of the common but easily avoidable mistakes include adding multiple SKUs into a single slot andcounting case packs as a single item instead of the individual pieces
Get these inventory management basics right and you will set forth your ecommerce marketplace business on a solid footing.
Capillary has partnered with Amazon to help marketplace sellers to maximize profits and revenue from their ecommerce business through easy and efficient inventory management. You can benefit from this partnership with a free catalogue service for 1 month to launch your business on Amazon. Now reach crores of customers. Sign-up here.
Exponential advances in technology, the proliferation of digital devices and constant connectivity has given rise to a new legion of consumers who live in the Easyverse™. These Connected Customers expect fast, easy and personalized experiences anywhere, anytime.
The imperative for brands to understand the ‘Connected Customer’
APAC’s digital economy is expected to top $1.2 trillion by 2021. To cash in on this multi-billion dollar opportunity, brands will need to get a deep understanding of the Connected Customer.
Furthermore, the term ‘experience’ has become more important than ever before for brands. In an era of minimal product differentiation and diminishing brand loyalty, customer experience and service will spell the difference between growth and a painful downward spiral into irrelevance.
But, do retailers and brands have what it takes to meet the expectations of the ‘Connected Customer’?
To answer this, we invited business leaders and brand experts Maya Hari (VP, Twitter, APAC) and Johan Vracken (MD, Nielsen, Singapore) to share exclusive insights about priorities, purchase preferences, and expectations of the new age, digital-savvy customer.
Understanding the Connected Customer
According to Nielsen, ecommerce in APAC is growing at a rapid pace with more than 98% of consumers making an online purchase against a global average of 95%.
Here are some more key takeaways and insights on the Connected Customers in APAC according to Johan Vracken (MD, Nielsen, Singapore) :
- On average they spend 6.5 hours online every day and expect brands to create a delightful experience at every turn
- SEA has 350 million online customers
- Online purchase of beauty & personal, packaged grocery, pet food is expected to surge in the next few years
- Connected consumers invest in experiences, spend heavily on travel, events, gaming, and consumer electronics
- Consumers in APAC choose brands based on loyalty programs, subscriptions, fast website and multiple delivery options
- APAC customers have an online purchase evolution that starts from small value products like skin care and eventually moves to premium and bulkier products
Within the Connected Customer universe, Millenials constitute a vital demography due to their numbers (50 per cent of the APAC population will fall in this age bracket by 2020) and their impact on global economy. However, engaging this consumer segment has been a marketing nightmare for brands across the globe.
Maya Hari (VP, Twitter, APAC) shared her insights on how brands can understand and connect with this mysterious demography in a better way
- Patience is in short supply with millennials – the average attention span is around 8 seconds
- 65% of millenials look for deals only at last minute
- One size fits all approach won’t work with the millenials – be it reward programs or brand communications
- Asian millennials are tech savvy, driven by purpose and obsessed with travel and fitness
- Compared to their western counterparts, Asian millenials focus more on quality rather than fancy packages
- 57% of millenials expect brands to stand for a social issue and they like brands that associate with movement
- Asian millennials are no longer in a position to afford housing easily, so 63% are not keen on leaving families
To attract, engage and retain the connected consumer, businesses need to look beyond conventional approaches and rely on digital solutions and cutting-edge technology to address changing consumer needs effectively. However, adopting technology alone is insufficient, the most significant change needed is in the mindset of business leaders.
To learn how to lead a digital-first organization, read the next part in our #Ready Insights series – The New Gen Leader
For most of human history, people have been good at predicting future technologies and innovations for say, 100 years ahead.
Today, predicting things even just 5 years down the line is usually an attempt at futility.
Thousands of existing technologies and industries are blending and merging in millions of ways in a singularity that looks all set to explode into a big bang of endless possibilities. However, even amidst rapidly changing variables, there are constants that have stood the test of time.
In a simple sense, technological advances merely served as a substrate for achieving what we humans loved so dearly from times of yore: expend minimal energy and save time. In fact, if you trace any invention or innovation down the rabbit hole to its moment of inception, it most likely would have been sparked by a single factor: convenience.
If you’re a retailer who is caught between agile, well-funded competitors and rapidly changing consumer behaviour, fret not as this single entity might be the perfect leading light beacon for a safe and successful voyage into the future.
So, without further ado, here are the top trends that will rule the future of ecommerce.
The blurring of digital and the real world
The question of whether we are living in a computer simulation is a long-standing one amongst astrophysicists and philosophers. Well, the question will seem more pertinent in the future thanks to AR companies like Next/Now, INDE and Groove Jones which are working feverishly to bring to life screenless digital projections and holograms on to the real world; rendering reality almost indistinguishable from the digital.
These technologies will have a massive impact and use-cases in ecommerce and retail landscape by easing purchase frictions by empowering consumers to negate the guess-work. A case in point is the ability to project life-like furniture in your living room to check if it matches the decor.
Intelligent digital assistants will become widespread
While the current set of AI assistants like Siri, Alexa and Cortana are impressive, the next generation of digital assistants is expected to be capable of almost human-like cognition. We are talking ‘HAL 9000’ from 2001 Space Odyssey and Samantha from ‘Her’ levels of cognitive and reasoning capabilities. Will they become the conduit for AI to go into overlord mode and enslave humanity? Elon Musk seems to think so but we’re not very sure. But they definitely will have huge ramifications in the ecommerce and retail space.
Macy ’s and a few other brands have already started piloting AI-based assistants in its stores. While its current capabilities are limited to guiding customers to the right section and offering product information, the day won’t be far when these assistants are capable of offering personalized fashion tips based on the occasion, local weather conditions, facial and body contour, and latest trends.
The shift from 2D view to 3D view of customer
As consumers start engaging with newer technologies like wearables, connected cars and IoT-enabled appliances, the amount of customer data available to brands will skyrocket in the next 10 years. This will accelerate the movement from the current 2D view of the customer to a 360-degree accurate view. When coupled with AI and ML innovations, these datasets will be able to predict consumer behaviour to almost 100% accuracy. The winners will leverage this data pool to build a persona for engaging with their customers in a contextually relevant and personalized way at every step in the shopping journey.
The pretzel-shaped shopping journey
The traditional linear purchase journey is already on its leg and in the next 5 years, brands can expect it to be totally phased out. Thanks to the proliferation of wearables and connected devices like cars, appliances and smart TVs , the consumers of the future will engage with brands across multiple platforms and devices in a pretzel-like twisted, crisscrossing loop. For retailers, the key here is to maintain a connected, unbroken, omnichannel journey and be ready for channel disconnects and reconnects anywhere, anytime.
Predictive analytics and curated shopping experiences
The barrage of data and the rise of complex AI systems will allow brands to develop accurate predictive models for recommendations and R&D for new development. This will give create the next step in curated shopping and subscription services to create personalized experiences to fit the exact needs of an individual customer. Predictive analytics is expected to have widespread retail use-cases like loyalty programs, customer engagement, in-store experience and fulfilment. For example, retail brands can devise a predictive model that will generate a customized discount code for customers who are at the risk of switching to a competitor.
Fulfilment will become a major differentiator
With the rising demand for same-day delivery, outdated supply-chain processes will no longer viable. The on-demand economy will force technology giants, logistic firms and retailers to combine forces to devise innovative delivery modes. Expect drones, driverless/autonomous vehicles, wearable and mobile technology and robots to be the most disruptive technologies. Alibaba, Amazon, UPS and Walmart are already experimenting with fully automated warehouses manned by an army of robots and drone delivery. In fact, drone traffic has reached significant levels in Japan that Rakuten Air Map has launched an unmanned traffic management platform in the country. Brands are also likely to engage in futuristic transportation modes like hyperloops and autonomous travel pods to further optimize their logistics network.
Hyperpersonalization will be the new norm
We are already witnessing personalization in many forms, but it doesn’t hold a candle to what is about to come. Consumers can expect a seamless, dynamic and real-time shifting world of content created around them by intelligent algorithms that know them better than they know themselves. Shoppers will have instant access to delivery dates, lowest shipping cost, and the best discount codes for multiple retailers within seconds. An agile ecommerce platform powered by AI and ML technologies will be critical in determining the success of your hypersonalization efforts. The winner may come down to brands that know the consumer the best, their product and delivery preferences, the likelihood of return shipments, and the packaging itself.
Omnichannel will evolve into the Omniverse
Omnichannel commerce is expected to become even more connected and seamless in a way that the individual blocks merge and finally fade into each other to create a unified real-time universe. This concurrent experience will involve consumers engaging with brands across digital and offline channels all at the same time. For instance, a customer walking into a retail store will be simultaneously comparing prices through his wearable smart glass.