Digital advancements in India have incredibly impacted how and what consumers are purchasing today. They are up to date with the happenings of the world on a real-time basis. This has shaped consumer behaviour leading to new demands, which many of the brands in India may have never looked at.
The fashion industry is the most impacted by this digital wave. Consumer behaviour is continuously advancing with the latest trends in fashion across the world. This has forced brands to make frequent changes not only in their brand messaging but also ‘positioning’ to stay relevant to their customers. Brands need to be equipped better to keep their brand message and position relevant to changing tastes and preferences.
The need of the hour is for brands to comprehend this change and predict these new demands. A key head start to this is always understanding what your customers are purchasing. What your consumers are seeking and what they are finally buying [Technology can bridge the gap between sought vs bought]. The moment your consumer starts seeking information is where the data points kick in. Once brands have real-time insights about their customers, they can undoubtedly utilize these trends for mutual benefit.
We at Blackberrys also sighted a similar opportunity in the Indian apparel market with our offerings – Mainline, Khakhi and Urban. The visibility of these sub-brands was low when compared to our parent image, which is primarily of a formal wear retailer. This obstructed us from gaining the full potential of our product mix. We had to position these brands in such a way as to give them enough visibility, without diluting our brand image. With data and actionable insights into our consumers and business, we were able to do just that. You will now see separate positioning and hence marketing activities dedicated for the three brands on account of our learnings. The brands are to have their own target groups and furthermore exclusive footprint and touch points, where consumers can truly experience these unique propositions.
An imperative step while repositioning a brand is to keep it relevant to the target group. When consumers perceive a product to be similar to the brand’s image, they will reason that what they know about the brand can be used to predict the product’s attributes, giving them reassurance when they transact with the brand. A data-driven approach lead by technology helps the brand understand its consumer profile and stay customer ready with their offerings.
Blackberrys’ current partnership with Capillary Technologies is helping us to design a targeted strategy for the relaunch of our brands. Capillary with their customer engagement expertise has contributed in developing and refining customer profiles for us to target.
Data-driven marketing efforts will bring the right customer profiles to your brand as opposed to mass marketing effort which brings a lot of quantity but not quality. Thus, it is very important for a brand to maintain its brand image to target the right set. Brands that are using consumer data and insights to develop customer-centric strategies and communicating new brand launches to a targeted set of customers have seen greater success.
The industry has started adopting technology but its use is limited to descriptive analysis rather than predictive analysis. We will see a move in the standpoint once brands begin to better understand its significance.
When I visited China over a decade ago, e-commerce was still in its infancy in the retail world. Some gentleman called Jack Ma had launched Alibaba in 1999, but there was no sensation around his name as yet! Mobile phones were quite popular and the urban Chinese possessed multiple devices (remember the Nokia Communicator and the Blackberry wave?!) but smartphones were yet to be born.
Now imagine the retail scene of the world’s most populated country then. National Bureau of Statistics of China capped total retail sales of consumer goods in the country at 59.5 Billion Yuan in 2004! This was largely dominated by the Departmental Stores and Hyper Markets chains such as BHG, Dashang and Sogo.
And then came the revolution that changed the retail space of the country – e-commerce took the world by storm. ‘Early-bird’ online retailers rode on the back of disruptive technology (availability of High speed internet, introduction of smart phones and increased internet penetration), increase in disposable income and spending capacity and innovative marketing strategies to grab a slice of the lucrative retail market pie.
With the total retail sales of Consumer Goods accounting for 33.2 Trillion Yuan in 2016, convenient online shopping especially from mobile, anytime-availability, heavy discounts, convenience of making payment through third party payment service providers like Alipay and WeChat Pay (both these account for 92 percent of China’s 18.8 trillion yuan mobile payment market) and flexible return policies gave e-commerce over 17 percent of total Retail Sales as of 2017 (from a meagre 1.26 percent back in 2004) – The e-commerce sales in 2017 were hovering around 5.16 Trillion Yuan.
We can positively establish that e-commerce is here to stay and though it has slowed down from its jaw-dropping stride and beginner’s steam, experts say future of e-commerce is safe for now. Euromonitor predicts the share of online shopping to touch 24 percent by 2020 while Goldman Sachs beats their optimism to benchmark a 31 percent market share for e-com. This will mean retaining the existing ones and increasing awareness and penetration to smaller cities and rural China. Over 80 percent of China’s urban adults already shop online.
This popularity of e-commerce is fueled by smartphones (over 52 percent customers buy using their phones on a weekly basis) and increasing influence of social media (the versatile WeChat). 79 percent of the Chinese customers (versus 46 percent globally) say that positive interactions with brand on social media have driven them to endorse the brand more, and 71 percent (versus 44 percent globally) have spent more as a result*. These changing shopping preferences of Millennials (preference for personalized services as well as convenience of picking up from nearby store) and adoption of digital payments (ref. graph below), has given a boost to a phenomenon called O2O.
Emergence of O2O and the two-way push for O2O
So what is this O2O? In lay man’s words, O2O (online-to-offline) is the link between say, looking things up online and doing the actual buying in the physical, tangible world. A web definition says ‘O2O is anything digital which brings people to shop offline, in real-world stores’.
Departmental Stores, Hypermarkets adopting O2O
With growth of online retail, departmental stores and hypermarkets have taken the biggest hit. The growth in the number of new store openings of the Top 100 FMCG players fell from 5.0 percent YoY in 2014 to 4.2 percent YoY in 2015. Many undertook store revamps while others had to shut down insolvent stores. To sustain in this ‘war’ with online retail, embracing online-to-offline (O2O) strategies is a common-sense initiative adopted by smarter hypermarkets and supermarkets. These ride-with-the-tide adopters are, among other things, rolling out O2O mobile-service platforms, forming strategic alliances to drive synergies and offering O2O delivery services.
“New Retail” Regime giving boost to O2O
To continue riding this e-commerce wave, Jack Ma talks of a concept called ‘New Retail’. “Pure e-commerce will be reduced to a traditional business and replaced by the concept of New Retail―the integration of online, offline, logistics and data across a single value chain” he had said in his October 2016 letter to Alibaba’s shareholders.
In just over a year, Alibaba went from opening its first physical store to acquiring a major department store chain in the country. Their quick and smart steps aim at achieving a sizable presence before their potential competitors do. Taking the Jack Ma ‘New Retail’ model to physical retail, Alibaba is taking bold steps in expanding its retail sales capabilities and setting an example for others – and it is only time before others across the globe join the bandwagon.
With the advent of Omnichannel, Retailers are focusing on providing consistent experience across multiple channels which include Online to Offline (O2O). Key trends in O2O are seen in the graph below.
This is very much how next-generation commerce is set to look like globally, with large retailers and niche category specialists leveraging technology to provide an integrated service with the consumer at its core.
In China, early adopters like HEMA and Ule have started to provide seamless O2O experience to their customers. While HEMA flawlessly incorporates online-offline systems to offer fresh food delivery services usually within 30 minutes, Ule is targeted on the rural sector. These type of platforms are connecting offline outlets with online/mobile services with extensive logistics partners. With data-driven technology and best practices of retail and e-commerce businesses, the Ules and the HEMAs are able to transform themselves from purely offline outlets to worthy competitors in the ‘New Retail’ paradigm.
Many of Capillary customers in China are making the shift and have realized how vital a robust platform is for providing a seamless online-to-offline and offline-to-online experience to the consumer. Capillary’s Retail CRM Solutions not only help retailers create seamless and connected experiences across consumer touch-points (viz. store, brand.com, Tmall, Wechat) but also ensure personalized consumer communications powered by its AI platform.
Retail market in China is humongous and vying for a fat piece of the pie is a natural instinct of sellers. New trends, new technology and newer ways to woo, convert and retain customers has always been an inspiration for innovation. Marketers wanting to keep their heads above the water have never shied away or stood against the tide. Trends like O2O have proven that revolution is welcome with open arms when customer satisfaction and service is the aim. For now the China O2O and omnichannel story are here to stay … what next … the dragon will tell!
Ecommerce is going through a challenging phase, not for customers, but for most of the pure play ecommerce firms. Discounting models have increased losses, reduced profitability of the model and has left the future of quite a few established ecommerce players in jeopardy. And all this at a stage when ecommerce has not even matured in most markets. What once seemed to be a smart move towards new customer acquisition has now become a necessary evil to keep sales going on. We frequently hear news of companies trying to reduce discounts in a bid to reduce losses, but most of us in the industry know how that works out.
Morning 1 – Company decides to slash discounts. End of day, company has made one third of regular sales. Pressure from investors! Competitor pressure.
Morning 2 – Alright folks! 80% discount on products is back!
So where did it go wrong? It all went wrong when someone thought that marketing department can be run by excel sheet experts – who do not understand the nuances of marketing, but are adept in looking at numbers and making excel sheet talk your language. There was an article some time back about how good performance marketing can actually be bad marketing. It made a lot of sense, because companies who boast of improving digital marketing efficiency never end up getting more sales.
Sales numbers remain stagnant (unless your sales drop by 50% and go up by 50% after couple of months and you make a PR out of it) even if your channel performance shows improvement month on month. So is it an attribution issue, or an issue of one channel cannibalizing other organic channels? Improved performance on retargeting for example can be due to your retargeting channel eating up sales which would have anyways come from CRM or customer making a purchase without any push. But how do you validate that? Go back to excel sheet, run multiple analysis and come up with a number driven reason? If excel sheets could make your future, every ecommerce company would be profitable.
The real reason is very simple. If you remove the essentials of marketing, brand building, increasing recall value, creating a Unique Selling Point for your brand, people will only come to your site for one thing – discounts! There will still be loyalists, who come to your site for the brands, or user experience, or the collection. They are the ones who gave you one third sale on Morning 1 above.
If you remove the essentials of marketing, brand building, increasing recall value, creating a Unique Selling Point for your brand, people will only come to your site for one thing – discounts!
Does brand building mean sending emails about new arrivals or brand launches to your most engaged users? I’m afraid not. A performance marketer in today’s scenario might jump up saying yes. But somewhere a true marketer would die once more in his grave.
I had the fortune of meeting quite a few ecommerce founders at various stages of business. Barring the top ones who are in a mad rush to survive, everyone else had the same input – no discounts please, or we want to build a healthy profitable ecommerce business that people love to buy from, or we want to create a strong and powerful brand with extremely strong recall. One brand owner even asked me – can you create a Starbucks for ecommerce for my business, where people visit without any discounts?
Yes we can and that is the true challenge, but are you willing to spend that time and money behind brand building to reach a point when loyalists drive your profits, and you introduce performance marketing for that incremental sales as and when needed, even if perpetually? Or is a mail from your competitor offering 30% extra discount going to break your will?
2017 will probably go down in history as the year when the China O2O story really took off. They range from Alibaba’s Hema supermarket to shared mobile phone chargers to shared umbrellas. In a bizarre marriage of IoT devices and QR codes, consummated by the 2 major payment platforms — WeChat and Alipay, it seems like a match made in heaven!
I’ve attempted to click and describe 7 hand-picked examples of O2O applications seen in and around Shanghai. I’ve myself used each one of these, and also there’s a couple of these that I swear by. So, here they go:
No O2O story can start without mentioning Alibaba. And this one (the mother of all O2O use cases) is part of Alibaba’s “New Retail” initiative to seamlessly blend offline and Online retail experiences. With a chain of Supermarkets named “HEMA”, Alibaba has entered into the hyper-local groceries space, and how!
The aisles contain digital signages with QR codes, which can be scanned via HEMA App to “add items to a virtual cart”, while you are in the physical supermarket
You can checkout right there (via the App) and schedule your stuff to be delivered at any 30-min interval throughout the day
This is by far my favourite China O2O startup / story / use case. Ever since it first launched last year, I’ve been hooked to the service, and have ridden close to 500 Km already. Dock-less biking is healthier, greener, and convenient. Triple Whammy!
What I love about them is that they stick to their mission, and are constantly innovating on the User Experience. For example:
You can Reserve a bike for 10 min while you walk to it.
You get extra points (redeemable) for parking your bike at appropriate positions
And my favourite one: During this Christmas Week, the usual “beep-sound” while unlocking a bike has been replaced by “Jingle Bells!”
3. O2O Massage Chairs (you heard that right!)
Now these have been in Shanghai for a while. At least 3 years – and would be one of the earlier applications, given that massages are popular in China for therapeutic reasons. Right from being in the middle of malls to co-working spaces, they are seen quite frequently. The use case is simple:
Scan QR Code, use WeChat / Alipay to pay the 3 RMB for the 10-min massage.
The fancier ones also have an in-built screen that you can watch TV/content on as well. Which can also be purchased on-the-fly while you get the massage!
4. Umbrella-sharing (at Subway Stations)
Yes, you heard that right too! — Since the last monsoon seasons, we’ve seen a few of these “umbrella-sharing” startups sprouting up on metro/subway stations. The use case is:
Scan QR Code, register to be a member. Pick-up umbrella at Subway Station.
Drop-it-off at the closest convenience store (they’ve tied up with the likes of Family Mart / 7–11 / Lawson’s, etc)
The verdict is not out on this one yet. But it is an interesting take for sure. And really useful on those days when it suddenly starts pouring!
5. Anywhere charging for your phone
Phones are getting smarter. And battery life is getting shorter. So a few smart men decided to marry IOT with QR codes, and launch these chargers which can be rented at restaurants and coffee shops @ 1 RMB / hour. So, how does this one work?
Scan QR code, pay 1 RMB via WeChat / Alipay (usually with a 99 RMB deposit. Select Android / IPhone, and a drawer opens up with a portable charger inside.
Take it to your table, change for an hour, and then drop it back before you leave.
6. Passport / visa photos at Subway stations
Pretty simple and self-explanatory. It’s a small photo-booth. Fully automated. How does it work?
Stand in front of the camera. Get yourself clicked. Choose Visa / Passport Type, as needed. And Print!
The good part is, you also get a QR code ID. So the next time you need a few more photos, just walk up to any of the booths, retrieve your photo from their cloud DB, print and go!
7. Karaoke “in a Box”
Waiting for a movie to begin? Or for your turn at a popular restaurant? — No worries. Get into a “Karaoke Box”, scan QR code, select your songs and sing. These have been cropping up across malls in Shanghai.
Gamification: Your music can be rated, and you can compete with your friends by linking your social profiles.
Oh, and if (like me!) you love your own singing, you can immediately transfer the recording to your phone via WeChat!
Love the pace at which O2O innovation is happening here in China. And I’m sure there’s innovation happening elsewhere as well. Any interesting O2O examples you’d like to share?
In an intensely competitive retail environment where change is not just consistent but exponential and disruptive, staying responsive to evolving customer buying journeys is critical for us and our partner customers. Products can no longer be about simply creating a temporary fix to a market need but providing transformative, long-lasting solutions.
Although at Capillary we were capturing a lot of omnichannel consumer data for our customers through our retail CRM solutions, the data captured in offline stores was purely post-transactional in nature. Brands had no way of identifying potential customers among store visitors or optimizing their in-store experience proactively.
This was in sharp contrast to online retailers, who had easy access to pre-transactional customer data which included everything from how many people visited their online store to their browsing behavior, time spent on-site to pages most visited, even cart analysis, abandonment and final purchase.
These insights enable online retailers to influence and tweak customers’ shopping experiences in real-time, positively impacting sales.
Recognizing this, we sought to build on our existing CRM platform by developing a solution that could give stores similar strategic capabilities in an offline model. By proactively personalizing buyer journeys based on advanced customer identification, customer visual profiling, staff optimization and deep store insights, stores and brands would be empowered to positively impact in-store conversion rates.
This was the vision our team was entrusted to work upon.
To this end, our team crafted the larger ‘Instore Vision’ which projected the creation of ‘Smart Stores’ or digitally enabled offline stores where in-store shopping experiences could be customized by infusing online elements such as ‘Virtual Store Staff Assistants’. The entire Instore Vision was rooted in the basics of store performance i.e. Store Traffic Analysis. This entailed focusing and analyzing the very start of a customer’s in-store buying journey, to answer a question as old as the bricks and mortar that are used to build the stores themselves.
“How many people have visited my store?”
Customer footfalls, or store traffic, is by far one of the oldest and most important KPIs of any store.
The number of customers that visit a store is the first indicator of a store’s performance and potential sales. Conversion Rate, a key store performance indicator, derives wholly from footfall traffic.
What we realized, however, was that this data was woefully unavailable and inaccessible in any reliable, structured and, most importantly, accurate manner.
In most cases, data on a store’s foot traffic lacked transparency and was purely anecdotal, based on assumptions and estimates. This outlined a clear need for a footfall counter that could accurately capture the number of visitors at a store and process this data in real-time in a reliable, structured and actionable way.
Offline retailers often stress out trying to count footfall
Capillary vs. the World
Footfall counters are not a novel idea, yet stores and brands still grapple to assess basic footfall data.
We first had to research, in no small detail, all the different types and models of footfall counters available to retailers in the market. These finding helped us define the final product and our targets of achievement over the next few months.
We learnt that in order to be an absolute value-add to retailers, our footfall counter would have to be –
Cost-effective and scalable Our focus towards the Asian market meant we would have to make our footfall counter more affordable than existing options to deliver a positive ROI and not hurt bottom-lines.
Over 95% accurate with no cases of over-counting or false-positives. As against the 70% accuracy standard (actual) of many existing solutions that often resulted in cases of over-counting (counting the same visitor more than once) and false-positives (incorrectly identifying store staff or non-human objects as visitors), rendering them ineffective and unreliable in gauging store performance.
High on availability with 100% uptime and seamless connectivity To combat the issues of downtime due to outages on existing power-run, Ethernet-connected options which sometimes led to incomplete data being captured.
We knew that achieving this would by no means be an easy feat, as we were going down a path riddled with challenges that many have tried to take on, but in true Capillary spirit, we were excited to make it happen.
With all systems a-go, our footfall counter, VisitorMetrix was scheduled for release in January 2017.
Breaking the Mold
Existing solutions which ran on multi-layer background extraction and blob detection technology delivered highly ambiguous results, being incapable of differentiating between human and non-human objects and were easily affected by changes in lighting conditions.
It was clear from the onset that existing technology would not allow for a product to be built on the lines that Capillary had hoped to achieve. We concurred that artificial intelligence would be the key differentiator that would set our solution apart and provide both the company, and its customers, a strategic competitive advantage.
With AI adoption in the retail industry still in its nascent stages, developing in-house, AI-based programs on which we were to center our first hardware solution was a bold move. However, given the ready and visionary talent at hand, we ventured on with no trepidation.
Behind the Scenes
Subrat Panda, Principal Architect and key driver of Capillary’s company-wide AI initiatives was pulled into the project right from the beginning. With a PhD in Computer Sciences, Subrat is representative of the human expertise within Capillary that drove the adoption of artificial intelligence in the development of VisitorMetrix.
Unlike other providers who banked on third-party software development solutions, Capillary had a clear objective to not just be a reseller but a proprietor of the technology we developed and utilized. Subrat and the other architects in our team went to work ensuring this by enhancing existing blob-detection technologies with advanced machine-learning algorithms. The team worked tirelessly at refining the algorithms VisitorMetrix would use by extensively training them on over 12 million images. This was to enable blog-detection programs to recognize visitors to a store in a more ‘intellectual’ way.
In order to do this, we didn’t follow the norm of depending on third-party, open-source libraries to obtain images but went about creating our own datasets based on actual store visitors and conditions for highly accurate results.
Doubts and turmoil
With only one month to go for the deadline, a status check revealed that while VisitorMetrix was checking the right boxes in terms of cost-effectiveness and connectivity, but it was still not hitting the mark on its key success target – accuracy.
At accuracy levels between 60% – 70%, a large number of false positive results were recorded, which meant the team was not confident about obtaining the desired IP.
The release date was consequently revised to April 2017, giving us only a 3-month leeway to identify issues with and rework the algorithms that would make VisitorMetrix effective.
The delay in release created a moment of uncertainty for us, and made me contemplate the improbability of meeting the revised deadline and making up for the loss of potential revenue an early release would have entailed.
Pictured: A human according to many blob detection algorithms
Ghosts of products past
VisitorMetrix was, incidentally, not our first foray into developing a hardware solution. The team’s first attempt at a hardware product was in 2015 with a Wi-Fi beacon which was to be used for geo-location, in-store customer identification and in-store engagement. Despite having invested heavily in hardware and achieving the results we hoped for, ultimately the technology we used had become redundant by the time the product was ready. iOS devices were now transmitting scrambled MAC IDs, hence we were unable to identify store visitors. The entire Wi-Fi-beacon market had tanked by then, making our product unviable even before its launch.
This incident, however, was just a chink in the Capillary armor. Far from being deterred, the team welcomed the challenge to roll the dice once again with VisitorMetrix. This time we knew we shouldn’t build a product that relied quite heavily on other third party systems but build as much capabilities as possible in-house to have the most control over product performance.
The AI training montage
Albeit having been demoralized by the delayed launch date, we ploughed on believing an accuracy target of 95% was achievable by fine-tuning the way images were captured and processed. We revisited our previously captured footage through the auxiliary camera in the device and after careful analysis it was revealed that in-store conditions such as lighting, shadows, reflections or objects in the external store periphery such as trees or cars for example, still affected the way images were captured, resulting in skewed accuracy results.
Having identified the glitch, we decided we had to ditch blog-detection completely. We were not going to achieve the accuracy we were hoping for with this. The team now rallied together to rework the algorithms, now relying on computer vision instead of blog detection or background extraction. With machine learning algorithms we were certain we’ll enable the system to accurately differentiate between human and non-human objects such as carts, shadows, etc.
We set up devices at 40+ stores to capture real-life visitor images. This included auxiliary devices to capture walk-ins and walk-outs that together yielded over 12,000 hours of video footage. Not only was this footage impressively extensive, but varied.
VisitorMetrix is trained to recognise just human images, for now…
Over 3 million images of this database were used to train the algorithms to identify humans from the top-view and 9 million impressions of non-human objects such as shopping carts, bottles, helmets etc. (that normally distorted results) were used to train the algorithms to differentiate between human and non-human objects.
Further rounds of intense image training followed until the algorithms were rendered capable of identifying visitors to the store accurately irrespective of store conditions. Now that the IP was almost within reach, it was imperative we didn’t leave anything to chance.
We were determined to make the April release an absolute success. The team, which is essentially an in-house unit, took dedication and collective effort to a whole new level when they decided to go into stores themselves to test and ensure product viability for customers under real-time circumstances.
Accuracy Realised
This exercise in perseverance put us on the right track and at the close of the three-month period, VisitorMetrix had achieved an astounding accuracy rate of over 95% having eliminated all cases of over-counting and false positive results.
This was the technological breakthrough the team had been working for and after 9 months of sheer dedication, VisitorMetrix was on schedule for an April release as Capillary’s first fully AI-powered hardware product.
The team didn’t rest there though. After having successfully developed and launched VisitorMetrix, the team immediately switched gears to ensuring the people counter was not just technologically but also market viable as well.
The 6 months following the release saw the team focus all their efforts on stabilizing the product to ensure optimal functioning under real-time conditions and fine tuning the device to account for full-functionality on all counts.
VisitorMetrix team working over the weekend to ship the first batch of devices. From left, Saurav Behera ( Senior Firmware Engineer), Sumandeep Banerjee (Principal Engineer) along with the first interns of the InstoreAI team, Harsheel and Ruchita
VisitorMetrix – The Launch
VisitorMetrix was officially launched to the market on October 8th, 2017.
At the time of its launch, VisitorMetrix was exactly what we had envisioned it to be viz. an AI-powered footfall counter that was–
Cost-effective – At a unit cost way under 250 USD, VisitorMetrix incurs very low set-up costs, making it both an affordable and scalable option for stores across all models.
Accurate – VisitorMetrix provides over 95% accurate results with zero cases of over-counting or false-positives which was important for us.
High on uptime – VisitorMetrix offers 100% availability or uptime via WiFi or LAN networks for uninterrupted connectivity.
Low on maintenance – VisitorMetrix requires zero maintenance and is easily configurable with remote troubleshooting
Looking Ahead
Since its launch, VisitorMetrix has found a home in over 300 stores, across 40 cities and its growing demand stands testimony to its commercial viability.
VF Brands and Promod were among first major retailers to deploy the footfall counter to their stores. In our webinar, “How AI can bring power back to stores”, Pankaj Agarwal, Retail Director of VF Brands spoke to the veracity of how VisitorMetrix drove a notable increase in store sales, based on customer traffic patterns.
Even as VisitorMetrix continues to gain market traction, our teams have already moved on to realizing the next phase of the Instore Vision offering, buoyed by the success of creating Capillary’s first AI-powered hardware solution; a win for both Capillary and our customers.
Retailers once had it easy. Little competition, loyal customers and easy sales. Then came an explosion in retail; great for consumers but not so good for retailers! The days of easy sale were in the past and retailers now had to work hard to build and retain a loyal base.
The first wave in retail was all about the data. Collecting data, analysing data, and using insights and campaign tools to personalize engagement and create a group of highly loyal customers. Next up was the shift to online. E-commerce and a connected experience both online and offline became a must have to be able to provide consumers with the experience they expected. This continues to play out and is often dubbed ‘going Omni-channel’.
While getting Omnichannel right is critical for survival, we are now seeing consumers evolving to the next wave.
The reality is that over 94% of sales still happen at brick and mortar stores.
While consumers are able to have a great personalized experience online, physical stores are woefully behind. Retailers have next to no data on what is happening in their stores. Who is the customer walking into the store? What has the customer browsed through but not purchased the last time? What does she like?
Having this data is a foundation to both provide the personalized experience consumers expect in stores as well as to maximizing operational efficiencies in a store.
The key to enabling this kind of data augmentation is through smart use of computer vision and natural language processing, artificial intelligence, we can create the tools to start getting the rich data and personalization available online, in offline stores.
I see this playing out in three stages
The first stage is to have accurate and real time data on visitors to your stores; and then integrating this with transaction data to be able to get insights on store staff effectiveness, power hours at your store, conversion rate, campaign effectiveness etc. I can’t emphasize enough the importance of accuracy! If the data is not accurate and reliable, the team will not trust the data and no action will be taken. Getting this level of accuracy and doing this cost-effectively is possible with smart use of AI and computer vision. A leading apparel brand saw over 5% incremental sales being generated by doing just this – getting accurate conversion data and working on improving this. Click below banner to read the entire story.
Next up is to understand how customers behave in store. Again AI, Computer Vision and Natural language processing based people counter and footfall counters can help you generate heat maps in store and answer questions like
Where do the customers tend to spend the most time in a store?
Which are the most popular sections and products in a store?
Which sections have poor conversions and how can those be improved by altering the store layout?
What paths do customers take in the store and how does the traffic flow through the various sections of the store?
Analyzing customer – store staff conversations using NLP to understand which products did customers ask for but unavailable at store or to understand how many customers asked for a discount or didn’t find their fit.
Once these are in place, it opens up doors to truly exciting and revolutionary applications of AI. With computer vision, Natural Language Processing and deep learning, we can now start doing amazing stuff. Imagine these:
Use AI to identify attributes like age, fit, clothing style and expression to get rich data on customer behavior and experiences in store. Do they like the item they browsed in store? How did they react to the store staff engagement?
Use Natural Language Processing to identify conversation trends, of course in a non-personally identifiable way. Are customers asking for black shirts? How many folks wanted a looser fit?
With their permission, and tagging customer IDs, you could identify your customer as soon as they walk into the store through facial recognition and have the store associate get instant information on the customer profile and their preferences, with clear suggestions on how to personalize the interaction and offerings. This would be a truly personalized and easy experience for the customer.
This is the power that retail technology is giving stores today, and it is amazing!
Capillary VisitorMetrix™, built on our AI platform Capillary Zero™, helps you unlock growth with accurate store performance insights such as conversion ratio, store power hours etc. so you can improve sales and optimize marketing. Visit link below to sign up for an exclusive pilot with CapillaryVisitorMetrix™ at your stores.
Keeping your customers happy and transforming them into brand advocates involves more than great service. How will you ensure that your customer keeps coming back to buy your product in the future? One great way to ensure returning customers is to have a powerful customer loyalty program. In fact, 76% of customers in the US agreed that a great loyalty program is a major factor in determining their shopping preferences.
Here are some more statistics around customer loyalty programs :
There are over 3.3 billion programs in the US currently
The cost of acquiring a new customer is 5 times higher than retaining an existing one
75% of US companies with a loyalty program generate a Return On Investment (ROI)
Profits from loyalty programs show an upward trend of up to 95%
83% of customers agree on the fact that loyalty programs are likely to make them continue to do business with certain brands
Existing customers spend approximately 67% more than new ones
What are the latest trends in customer loyalty programs?
The 3Cinteractive study says that 64% of brands reported an increase in customer loyalty programs in recent times. Loyalty programs are moving away from the traditional spend-and-get models to more sophisticated channels like omnichannel and multichannel programs, which recognize every interaction that the customer makes with the brand.
Here are some of the biggest and latest trends in customer loyalty programs
Omnichannel and Multichannel programs
The use of omnichannel and multichannel loyalty programs are proving to be very effective. Omnichannel loyalty programs help customers connect with the brand easily and seamlessly across different touch points. Consumers are given the opportunity to be rewarded for spends and brand engagement across all channels like app, website and social. The data that is captured through omnichannel loyalty programs help brands to offer personalized communication and experiences to their customers.
Beauty giant Tarte’s launched their “tarte <3 rewards” loyalty program, where they reward their customers for not only spending their money on their brand, but for sharing content and their experiences on social media, referring their friends and reading the emails sent by the company. These kinds of activities turn members into brand advocates, keeping them informed and engaged about the brand and helping to spread the good word around to a larger audience. The data that is captured about the members; how they spend their money and their engagement activities helps the brand to further amplify its marketing efforts.
Consumers expect brands to become more personalized
There is a connection between personalization and customer satisfaction. 67% of customers said that they are very happy when they are made to feel special and recognized through personalization. The satisfactory meter for customers who received personalized communication and offers is 2.7 times more than normal customers.
It was found that customers were willing to share their personalized data with brands in return for personalized experienced. This data can be leveraged to offer relevant promotions, upsell and cross-sell relevant products and services and make personalized recommendations.
Many resellers are using personalization in their marketing and customer strategies. Members of the DSW Rewards Program were sent an email to let them know how many points were required by them to receive a $10 certificate, were informed of other deals that they were eligible for and were given a detailed snapshot of the interactions that they have had with the brand over the last two years. This data included how many points they have earned, how much they have saved and since how long they have been loyalty members. This program was a huge success and saw a 64% increase in the openings of emails, a 13% increase in click-through rates and a 58.82% rise in customers who opened their emails and read them for about 15 seconds. This kind of a program not only keeps customers informed about their engagement, but it also encourages sales.
A sense of social responsibility is expected from brands
Customers expect brands to be socially responsible; to go beyond making profits, become drivers of change and become active in their respective communities. This has a major impact on customer loyalty, as customers are more likely to support businesses that have a purpose beyond profits and are ready to make a difference to their communities and the environment at large. A staggering 66% of customers were willing to pay more for products and services that come from companies that are committed to social and environmental causes and those that work towards bringing about a positive impact on society. Customer expectations and loyalty are better met when corporate social responsibility is incorporated into a loyalty program.
TOMS Shoes has a Passport Reward Program whereby their members have an option to redeem points towards a donation, a charitable cause or an initiative. For example, members can make a $25 donation to support the company’s after-school groups or community development programs. This option creates a goodwill amongst the members and helps them to emotionally connect with the brand.
Increase in brand partnerships
Partnerships amongst brands have taken center stage and are seen as a strategy for growth. This is also a great strategy to stay ahead of the competition as it allows brands add value to customers, beyond what they themselves can provide. The right kind of partnerships amongst brands provides new and exciting ways to reward customers. This, in turn, increases sales and loyalty.
For example, Wyndham Rewards, that is a loyalty program offered by the Wyndham group of hotels has partnered with Caesars Entertainment’s award-winning casino-based “Total Rewards” program. This allows members access to leading industry travel experiences, perks and benefits such as complimentary status match, one-of-a-kind hotels, restaurants and entertainment. This kind of partnership and branding also allows the hotel group to access potential new customers.
Premium loyalty programs are getting popular
Premium loyalty programs are getting very popular because members want to enjoy benefits and they do not mind paying extra for them. For example, Amazon Prime offers many benefits to its members, in the course of their loyalty programs. In a study conducted on consumers, 62% of respondents said that they were willing to consider joining a loyalty program, if their favourite retailer was offering them one. This percentage increased with the millennials. 75% of customers in the 18-24 age group and 77% between the ages of 25 and 34 years said that they would be willing to join a fee-based reward program. 47% of the audience that was studied said that rewards in a fee-based program were much better than rewards in a free program.
The PowerUp Rewards premium loyalty program by Gamestop takes pride in having more than 50 million members. Members of the program contributed three times the revenues of non-members. Recently, the brand introduced a new program, that is an upgrade from the current level (Pro). The new PowerUp Rewards Elite Pro membership costs $29.99 USD per year, which is double of what people pay for Pro. But members do not mind paying this amount because it provides them with additional perks, including free two-day shipping, $50 in exclusive monthly offers and discounts on pre-owned games and software. The brand received feedback from reward members who wanted to earn more points and subsequently enjoy more rewards and hence this program was introduced. The success of this program demonstrates that PowerUp Reward members see the value that they get in paying an additional fee. For gaming enthusiasts willing to go the extra mile, the brand encourages spend and loyalty.
Emotional loyalty is a huge factor in influencing purchases
A research that was recently conducted by Forrester found out that emotions are a very strong factor in loyalty programs and they in fact, drive them. This means that companies should invest in frameworks that help them understand emotions of their customers at various interaction points. Finding out what kind of behaviour elicits their consumers’ actions and emotions is a step in the right direction towards true loyalty.
The survey of marketers found out that most brands are trying to build an emotional connection with their most frequent shoppers.
Gallup conducted research where it was discovered that emotionally loyal customers were willing to be loyal to a brand, even if less-expensive alternatives were offered. During this research, they also found out that consumers with strong emotional connections to retailers were willing to visit their stores 32% more often and spend 46% more on the average bill value.
For example, to reward customers who showed an interest and demonstrated deeper emotional connections and a dedication to the show, “The Walking Dead”, the studio offered “money-can’t-buy” experiences and rewards. Leveraging a loyalty program, the brand encouraged fans to attain these unique rewards through further engagement. Members who accumulated the highest level of points as part of the program, enjoyed one-of-a-kind experiences, including VIP tickets to the “Talking Dead” show, a set tour and meet-and-greet with the cast.
How does Artificial Intelligence (AI) and Blockchain impact consumer loyalty programs?
Customer loyalty is changing constantly. Customers are increasingly demanding to be seen and heard as individuals, with specific needs and requirements. Using AI in loyalty programs can solve a whole lot of issues and concerns that plague the existing system.
How is AI used in these programs? It is the only way to reach out to huge numbers of customers that run into thousands and millions. It is the best way to capture an individual’s need, intentions or preferences, in real-time. Each individual’s data is captured, thereby putting together a collective database that delivers information which goes far beyond numbers. Technology like speech recognition, dialogue management, natural language processing etc. are used to create intelligent assistants. These intelligent assistants can interact easily and naturally with human beings and can help them with accessing information and completing tasks.
For example, AI can help power Virtual Assistants who can monitor specific customer behavior and reward them for it. This in-turn inspires loyalty in customers and all this can be done without the intervention of human beings.
Another good feature of the integration of AI is that it offers security against cybercrimes, fraud and theft. AI systems can also augment employee performances, which frees them from mundane tasks, so that they can focus on important areas that maximizes customer experience.
The benefits of using Blockchain in customer loyalty programs are cost-efficiency, speed and real-time response capabilities, both for participants and stakeholders. An efficient way to reduce management costs and to bring about transparency in transactions are some of the other benefits that are offered through this technology. Cryptography that is available in Blockchain Technology exposes fraudulent transactions and flags off mistakes. They also offer customers the ability to create, redeem and exchange reward points across partners and different vendors.
To stay relevant, loyalty programs should leverage the latest technology and always keep customers at the center of their focus.
