Over time, the increase in the number of avenues through which consumers interact with your brand has contributed to the exponential increase in the amount of data at the retailer’s disposal. And it is showing no signs of abating. Although it adds several layers of complexity in how you deal with this constant stream of data, tapping into it to uncover ‘in-the-moment’ opportunities will make it all worthwhile.

However, that’s easier said than done. Clearly, the real challenge lies in how you can harness the right online and offline data efficiently to better understand and engage customers at precisely the right time. This capability alone will separate retailers that thrive from the rest in the years ahead.

That begs the question, which data is useful to deliver hyper-personalized experiences that consumers expect from retailers today? How can it be harnessed? Here are some examples:

Granted online data is easier to gather and use – to deliver personalized experiences. But simply using in-store purchase data can significantly enhance the shopper experience. After all the lines have blurred, and consumers shift effortlessly between the website, mobile app, or the physical store.

Click and mortar stores can gain a significant advantage over pure-play commerce stores if they can better utilize this data for an omnichannel personalized experience. The key however is arriving at the right mix of offline and online data with a reasonable level of consistency. AI-driven omnichannel personalization solutions can help. Believe it or not, a simple tag like “products trending in-store” can reduce friction through the online shopping journey.

Some of our customers have reported an increase of over 2% in their average order values just by bringing offline data to refine the online experience. To add to that, more unified experiences are offered to customers by way of recommendations across product categories. For instance, the AI can automatically filter out poorly rated footwear products and eliminate them from recommending those to consumers in their email campaign with offers for matching clothes. Besides, the system does all this automatically, eliminating the need for manual merchandising or interventions.

Sure, you want to deliver individualized experiences, but at the same time, an area that is worth focusing on for retailers when it comes to increasing profits and margins is back-office data. Some avenues to explore:

• Margin data: Using this data, customers can be guided to higher margin products through boosted search results, promotional offers, and recommendation blocks. These high-margin products when intelligently combined with loss leaders have shown they can boost profitability. These can easily be tested by the system.
• Brand: Brand affinity is a great way to boost margins. Similar brands at similar price points can be linked up and budget brands can be suppressed within search results to drive more return visits, perceived relevance, and larger basket sizes. The critical part is that the AI delivers the best results for affinity-based products by determining the right price point ranges for related products.
• Regional store/inventory: Using regional inventory data can help in adding a layer of personalization to the online buying experience, help drive demand and reduce out-of-stock disappointment, while driving up profitable store visits.

One of the common challenges with data has to do with poor product feed data, whether it is to do with quality or depth of it. Many approaches can be used to expand data to increase sales.

• Starting simple helps. For example, you might have a range of kitchen accessories, let’s say for a brand called “Denver”. Using simple rules, you can boost the product name “Denver” by promoting other items from that brand. The rule would have an if-then clause like ‘if the product name contains the term “Denver”, then boost other products containing the term “Denver” from the same category.’
• Deep Learning AI lets you understand how customers research your products before making a purchase decision. Descriptions, comments, social posts, tags, and other such data are crucial in helping customers find their choice of product, while also helping them realize their ‘intent’ of what they want – all through the power of AI.

When you link ‘similar’ products together based on common concepts (e.g., easy, simple, long-lasting, rugged, etc.) and start to understand the ‘why’ behind a purchase decision (e.g., long-lasting frying pan for someone who camps out a lot), you get access to a whole host of products that you might have never guessed would be a perfect fit for some of your customers.

It’s true that data can be invaluable, but you do not need to use everything that is available. Begin by using small sets of relevant data as mentioned above. When coupled with AI, it can give your business a real shot in the arm.

Channels are dead. Period.

Omnichannel experiences are now non-negotiable. Retail technology investments saw a 300% YoY increase in Q2 ‘21 to $31.5 billion, as more consumers turned into omnichannel shoppers (CB Insights).

Consumers are no longer following a linear path to purchase. Retailers are trying to solve the pressing issues of broken customer journeys and inconsistent engagement. And these issues often arise when a consumer switches between different online and offline channels – a behavior that has become commonplace in the post-pandemic world.

Today’s channel-agnostic consumers expect to be able to seamlessly switch between channels. They expect to progress in their journey without friction points such as repeatedly having to specify their current need, price point, brand or taste preference, and so on.

Retailers across key functions have to make countless decisions and course-correct as context changes. What products to recommend, what is the right assortment mix for the season, what items to re-stock, how much to sell them for, and when and how much to mark down are some of these crucial decisions that impact customer experience, profitability, and key business metrics.

Another key challenge that retailers are facing today is the inability to personalize an experience as per a consumer’s real-time intent and context. A Forrester report revealed that personalization ranked the highest in the list of tech investments in 2021.

In fact, 89% of digital businesses are investing in personalization. But there’s a problem. Only 40% of consumers feel that the information they get from brands is relevant to their tastes and interests. This massive gap exists because personalization has evolved from tactical segmentation to strategic individualization, and most retailers are playing catch-up.

To solve these challenges, retailers must adopt a more forward-looking approach to decision-making. Retailers are no strangers to big data analytics. This technology has been a focus area for the better part of the last decade. But the problem even today is, most companies are using data primarily to make tactical business decisions, making them reactive rather than proactive with their data strategy. They’re still data-driven instead of decisioning-led.

Point solutions don’t cut it, as they hinder retailers from future-proofing their digital strategy, creating new competitive advantages to remain relevant, and staying ahead of ever-evolving customer behaviors. What’s needed today is AI-powered decision-making at scale across marketing, eCommerce, merchandising, inventory management, pricing, and supplier management.

To meet this need, we’re going to unveil the industry’s ONLY Algorithmic Decisioning Platform at NRF 2022 – Retail’s Big Show, Booth #6403. Algorithms take away the pain of decision-making from individuals across key retail functions by not just making decisions but also executing them autonomously. Here’s how the platform cuts across different functions.

ADA Global’s Algorithmic Decisioning Platform

• Create a unified, seamless experience across search, recommendations, category pages, content, email, and offline channels
• Hyper-personalize search results and category pages based on behavioral data, such that no two shoppers see the same results
• Help shoppers find visually similar products and provide complete-the-look recommendations based on product images, without manual merchandising
• Fill recommendation gaps even for products with no behavioral data and fast-changing catalogs
• Pre-load a customer’s basket with items they’re likely to re-buy and boost basket sizes

Aldi, one of our clients, is surfacing more of their catalog to shoppers without compromising on relevance, achieving 46% higher revenue per visitor and 10% higher average order value. See how Aldi achieved these results.

• Act on unified customer data across stores and digital for a single source of truth
• Drive real-time, journey-based engagement across email, SMS, stores, mobile app, online store, contact center, kiosk, direct mail, and social media
• Auto-discover micro-segments
• Acquire new customers that resemble their star customers
• Grow loyalty and frequency through individualized interactions
• Spot and reverse churn behavior
• Algorithmically test campaigns and auto-optimize without manual A/B tests

A supermarket chain and a Forbes 500 private brand with over 100 stores achieved 3% incremental sales and 2x digital adoption with Algorithmic Decisioning. See how Big Y doubled digital adoption.

• Accurately forecast demand based on customer, economic, and social environment
• Plan assortments and availability across stores and digital channels based on customers’ current and evolving needs
• Optimize markdowns to reduce inventory and margin erosion
• Steer insights-driven supplier collaboration by automating end-to-end processes, from onboarding and ordering to pricing and promotions
• Curate and segment merchandise for a hyper-local and hyper-personal experience

A multinational conglomerate leveraged our Algorithmic Decisioning Platform for merchandising, resulting in a 95% improvement in sales forecast accuracy, 12% increase in sell throughs, and 30% reduction in excess stock. See how this retailer cut excess stock by 30%.

• Leverage a lakehouse of structured and unstructured data for a single source of truth
• Use pre-built, retail-specific models to unearth deep insights for varying business needs and digital transformation strategies
• Get easy access to persistent, unified data for advanced investigation and custom analyses, with minimum data preparation
• Drive robust data governance with access control and encryption from a single point, eliminating the overheads of managing data governance on multiple tools

Retail decisions need to become self-driving – where algorithms consider the context, understand your goals, and orchestrate decisions that put the customer first. More than 400 retailers and brands across the world have achieved this target state with ADA Global. Want to learn more?

Science has been playing an increasingly important role in marketing, not just in data science and analytics but also in psychology. People like to follow the majority and find comfort in numbers. We’re influenced by what others are doing, and view a behavior or action as appropriate if we see others doing it as well. This is why we consult our friends when booking a vacation or buy products endorsed by celebrities.

However, research shows that the opinion of strangers influences us just as much as that of an expert or a friend. Which is why we care to read product or movie reviews, adopt new habits such as yoga, or have an instant inclination for NYT bestsellers. As a new meditator, this tiny message on my meditation app, that tells me others are practicing now, subtly nudges me to continue without doubting the process.

To help marketers and retailers leverage this powerful social phenomenon and make their eCommerce experience more compelling, we’re introducing real-time social proof messaging. Online shopping is often cold and lacks experiential elements such as cues on what products others in the store are shopping for, or what mains and entrees other diners are ordering. Mid-funnel drops due to indecision are common, and social proof messaging is a small way to arrest these drops.

Timely social/urgency messaging helps shoppers decide, by signalling if the product they are viewing is in-demand. Retailers can easily customize the messaging and choose from real-time metrics such as number of views, purchases, add-to-carts, or even inventory levels of a given product. In addition, social proof messaging can be used on pages across the shopping funnel – home page, category pages, product pages, or even at the bottom of the funnel on add-to-cart pages.

Here are some sample messages you can show:

• 15 people bought this today
• Selling fast – in 34 carts right now
• Low stock – only 3 left and in 21 carts right now
• On fire – viewed 67 times in the last 30 minutes

What’s more, you can make the messages more personalized to a shopper. For example, ‘8 people added to cart since your last visit’ or ‘Popular with skaters – viewed 48 times in 10 minutes’.

Creating a new message is simple and marketer-friendly, and no developers are harmed in the process. 🙂

All it takes is a few clicks and your knowledge of the customers.

1. Select a template from the library
2. Choose the design, colors, look and feel
3. Select the relevant segments, or even a geo-location or an attribute
4. Pick the pages to display it on, set thresholds for different metrics, and that’s it!

Simple A/B testing allows marketers and merchandisers to test different messaging and metrics, locations and variations – and all of this without the need for any coding skills.

Watch how the set up works.

As they say, your customers are your best marketers – leverage the power of social proof to build conviction and grow sales.

It might be called “artificial” intelligence, but the connections AI enables with QSR customers are not just real, but real revenue-drivers.

Personalization is key to the online ordering experience for QSRs. The promise of short delivery times, hygienic service and great food are crucial elements, but customers also seek that “human element” even in the virtual world. So, how then, can QSRs use AI to keep customers engaged and coming back for more?

It all starts with the algorithms. AI algorithms help power QSR marketing strategy with intelligence and this drives customer loyalty. Knowledge of customers’ behaviors, tastes, preferences, geographic location and other factors, allows for focused personalized retail and eventual growth in sales.

The current crisis has accelerated the adoption of digital interfaces across quick service, and that’s a behavioral change that’s likely to stick around. It’s now or never then, for QSRs to make the shift to providing customers with a seamless ordering experience across digital channels. And, when customers do order online, the QSRs that are most adept at personalizing that experience will be the ones most capable of going the competitive distance.

Customer data platforms — powered by algorithms — sift through the treasure trove of customer data collected through various digital touchpoints to assemble a unified view of customers, along with their wants and needs.

In a quick data study, for example, QSR “regulars,” who tended to experiment within a narrow range of options, were found to comprise the single largest category of customers. QSRs that know those customers’ food choice and customization preferences, as well as any food allergies or diet restrictions, birthdays and other special occasions, and even work patterns will build the best sense of bonhomie and customer connection.

Domino’s did this well with their emoji-based ordering mechanism. The campaign required customers to tweet or text Domino’s a pizza emoji to let them know they’re hungry for some pie.

Regular patrons could also select the brand’s “easy pizza” option, that made it that much easier to order their favorites, based on their past preferences. And though, that option required customers to create an account exclusively with Domino’s, the campaign was deemed successful and serves as an example of how a QSR connected emotionally with customers, in a socially relevant manner.

In a world full of merging identities, people are still defining their own personal style. And, what better way for QSR’s to promote individual sense of style than with a build-your-own concept?

With the help of AI, businesses can now canvas their customers, understand their meal preferences and even make room for a little experimentation with DIY menu options. Subway, Wendy’s, Starbucks and Burger King are already excelling at this task, but even smaller fledgling businesses are starting to get familiar with this approach as a way to build bridges with customers.

Hyper-personalization allows brands to extend offers, products, recipes and combinations that resonate with customers. It starts by gaining deep customer insights through the use of advanced algorithms for very specific customer segmentation, segment analysis and movement. This intelligence is then applied to driving personalized engagement with customers in their journeys.

Artificial intelligence technology can capture real-time dynamics through multiple digital channels so QSRs can tailor their content personalization quickly and effectively via their apps, social media, websites and other communication channels.

Key to the above is getting customers to sign up and allow for syncing accounts and identities. Most accounts then come with an order history or favorites list that can be used to curate ecommerce personalization efforts. And whether the data indicates the customer typically orders as a family or individual, social proof messages can be curated to best prompt them to order from the convenience of home with just one click.

With data indicating that the pandemic has triggered a 24% spike in time spent on digital media (mobile/web), QSRs are naturally heading to mobile to reel customers in.

Convenience has always made the QSR’s world go ’round and open time personalization mobile app and ordering experiences are key to that. Through built-in technology, QSRs can easily put customers on a path from initial purchasers, to loyal patrons with repeat orders. QSRs that use geo-fencing and location-based technology in their apps, like Burger King, Starbucks and Taco Bell, have been able to garner prepaid purchase orders in this way.

For instance, Taco Bell full-feature mobile-ordering app uses GPS location information to identify nearby customers and prompt them to place their orders ahead of time. Additionally, the brand can provide those customers with ai email personalization based on their past ordering behavior, which also expedites the customer’s journey.

Artificial intelligence and predictive analysis is embedded into digital touchpoints that can study, learn, understand and even recognize our voices. AI algorithms can help QSRs create a differentiated experience for their customers across the highly competitive quick-service landscape.

Mining big data from digital channels like websites, mobile applications and more provides endless possibilities for personalized product recommendations. More still, this information can help QSRs connect with even the most infrequent of patrons to help them understand how to order easily, quickly and conveniently from brands they know and those they’ve yet to discover.

The article first published in QSR Web

The pandemic has changed the dynamics of the retail world which has taken on a new avatar with growing smartphone penetration and disruptive technologies, one of which is Composite Artificial Intelligence (leveraging a multiplicity of AI methods in organic harmony with each other).

Deployment of AI in retail is expected to grow at a CAGR of 34.4% from 2020 to reach $19.9 billion by 2027, according to Meticulous Research. Gartner too had identified Composite AI as No. 1 Hype Cycle trend for 2020 and we think the feature rules in 2021 and beyond.

Why is the retail sector investing heavily in AI? The reason is not difficult to fathom. AI has opened up new opportunities and capabilities, speeded up processes and has made organizations agile and adaptable to changes in the future. To stay ahead of the competition, the retail sector is banking heavily on AI.

However, there are two key requirements to doing AI well: Firstly, a deep understanding of the sector being served and what we need to address. The key problem of AI is not coming up with the right answer; it is knowing what question to ask.

Secondly, a data-driven approach that carefully identifies the outcomes to optimize for, the inputs to use, the evaluation criteria, and the appropriate technologies to use. Depending on the problem, this may involve a variety of approaches such as rules-based systems, unsupervised and supervised machine learning, natural language processing (NLP), optimization techniques, graph techniques, deep learning etc.

A rigorous and data-driven approach ensures that the models and data flows we design are the ones that lead to truly successful outcomes.

In e-commerce there is no one-size-fits-all for AI deployment. Specific needs of retail and e-commerce have specific technologies. Composite AI harnesses powerful technologies such as deep learning for images and natural text and contextual bandits for nimble and sophisticated optimization. It facilitates rich customization that is controllable or automated as the client desires.

The benefits of Composite AI are many. It allows retail enterprises to have intelligent customer interactions, leading to personalized ecommerce experience of customer experience, cross-selling and up-selling; merchandising; catalog handling; and end-user satisfaction.

Customer journeys make important data and retail thrives on seamless and frictionless customer journeys across channels and Composite AI acts as an enhancer and a facilitator for quick launches. Streaming live product recommendations driven by location, device, contextual understanding from clickstream/NLP chat conversations become a selling point.

The Deep Learning approach delivers right recommendations in e-commerce where collaborative filtering does not succeed in making the right predictions, particularly where products do not have any user interaction at all. There needs to be a deep language model to understand cross-sell associations between products, based on product descriptions, trained by actual purchase events.

This new approach removes constraints linked with traditional recommendations that may not work for retailers with sparse data. It also helps product discovery optimization by capturing user’s preferences through a product’s visual features and textual description.

It can train a deep image network for “Complete the Look” recommendations for fashion/apparel items, working on a combination of clothing items and accessories that go well together (based on actual outfits) and then uses this trained model to propose complementary items, with phenomenal results.

The Contextual Bandits approach utilizes available context information to pick the optimal strategy for the page/placement at hand, in real time. It targets different optimization metrics for different stages of the shopping funnel: optimizing click-through-rate (CTR) while browsing items and optimizing revenue during/after purchase.

This is the answer for the recommendation space where there are many layers such as target engagement; inputs from point of sale etc.

Retailers have benefited by deploying DeepRecs NLP and DeepRecs Visual AI. A Japanese entertainment online retailer with over 2 million products reported that 96 % of the products did not get sufficient view or have purchase history but with DeepRecs NLP (recommendations use text data i.e., product descriptions and products with similar affinities) the products propped up for shoppers. The CTR for the retailer increased +4.99%, while the average order value (AOV) was up by over 6.22% and the revenue per visitor (RPV) grew by over 7.29 %.

A French fashion retailer reported over 19% RPMI and 40% CTR with store-like recommendations coming up on e-commerce, leveraging visual characteristics of a product. This resulted in generating content personalization similar product recommendations based on visual similarity.

For transformative results from Composite AI, enterprises and vendors need to be immersed in the business space and have thorough knowledge of the key applications and pain points. A Composite AI approach needs a “composite architecture,” factoring in packaged business capabilities that run atop a flexible data fabric.  Retail needs to have a deep understanding of Deep Learning to harvest rich dividends.

The article first published in RIS News