As an eCommerce brand, many of the items in your inventory might be the kind that fly off the virtual shelves in minutes and have to be constantly replenished. There might also be some products that remain undiscovered by users for several reasons, such as the product is new, niche, or seasonal. For such cases, users need a little help with their product discovery optimization.

New products most likely get ignored by your personalization engine. This is because personalization platforms typically start recommending a product only after it has gathered enough behavioral data around it, such as pageviews, click-throughs, add-to-carts, purchases, etc.

Without such behavioral data, these new or seasonal products remain buried on your site. This scenario also applies to long-tail products—items that are niche, have limited stock, or are not commonly searched for or bought by customers.

The traditional approach is to manually add merchandising rules to boost them to the top of search results pages, category pages, or homepage. However, this approach isn’t scalable.

Why Rule-based Inventory Visibility Isn’t Ideal

If you’re using merchandising rules to promote visibility for new products, some of the problems you’ll have to contend with are:

1. If you have a site where new SKUs are constantly being added, manual updating is a tedious task.
2. Time is of the essence for certain products that are seasonal or holiday based. This means your teams will not get enough time to manually create rules for every single new product on the site.
3. You also have to remember to turn off the rules when the products start appearing in search results organically or when the season for those products is over.
4. The boosted products could show up in irrelevant search queries. Worse, they could inundate a page and bury the relevant results. Both these scenarios will frustrate at least two out of your five customers and drive them away from your brand.

Altogether, the manual merchandising rules approach puts pressure on your resources and wastes time and money that can be used more productively elsewhere. So, replace this manual and tedious job with an AI-powered personalization engine.

How NLP Improves Visibility for New and Long-tail Products

Natural Language Processing (NLP) is a deep learning algorithm that processes text data to understand the meaning of the words as a human would, instead of employing the old-fashioned keyword-matching technique.

In the context of personalization, NLPs read through texts related to the product, such as descriptions and customer reviews, extracts associated attributes and text vectors, and forms associations between products based on feature complements and semantic similarity.

Here’s an example. Say a home décor store has added 300 new Christmas-related products to their inventory. They switch on the NLP strategies on their AI-powered personalization platform, and the platform analyzes text data about these products in a matter of minutes. See the adjacent image for an example of how text is analyzed and word associations are formed.

Though these products do not contain the obvious word “Christmas”, NLP associates words such as red, green, holiday, snowy, festive, merry, Xmas, Santa Claus, secret Santa, chocolate, gift, and desserts with the holiday.

And once they become visible and users interact with the product, the platform has the behavioral data necessary to organically recommend them, which means that NLP also becomes a “trainer” for other personalization strategies.

In addition, NLP can also re-rank products in real time during the same user’s session, based on whether the person views such products or not.

All this ensures that new and long-tail products are personalized, users see relevant content personalization cross-sell offers from the start, and personalized product recommendations become more relevant as users move through the purchase funnel.

Time to Get an NLP-based Recommendation Platform

The ADA Global DeepRecs™ platform leverages NLP algorithms to make the right ecommerce search personalization product recommendations. Because of the retail expertise already “learned” by our Xen AI by working with dozens of customers, DeepRecs™ is already production-ready and scalable with several NLP word vectors and semantic knowledge that you can bank on.

The platform can use your data to create custom models for your products, factoring in actual co-purchase behavior. It ensures that new, seasonal, and long-tail products are recommended at the right time—for search queries as well as for recommendations of dynamic content personalization, similar products, cross-sell, and upsell.

Get a demo to see how your business can benefit from DeepRecs NLP.

When someone asks a marketer how to go about personalizing customer experiences, their first answer is likely to be targeting. For example, special promotions targeted at “new users”, or additional incentives on in-cart products for “cart abandoners”.

This kind of targeting works at a user segment level. Segmentation refers to the grouping of users into different cohorts with similar parameters. These parameters could be based on demographics (age, gender, location, income level, etc.), technographics (device, browser), behaviors (purchase history, search history), or psychographics (affinities, preferences, attitudes, values).

Standard personalization platforms work on rule-based targeting: you add rules to combine and create segments and target the right customer with the right products and services.

However, this does not allow for a deep understanding of the customer. Some percentage of individuals in each segment will be different from the rest in many ways, and to convert these users into customers, you need to go beyond rule-based segments.

Marketers can no longer ignore the power of personalization.

• The purchase decision of 86% of US consumers have been influenced in some way by personalization. At the same time, almost three-fourths of retailers say personalization has increased their sales.
• A whopping 91% of customers in North America and Europe are more likely to shop with brands that personalize experiences for them.
• In fact, 83% of consumers in North America and Europe are willing to share their personal data for a personalized experience.

A look at early adopters like Amazon is enough to tell you why users prefer personalized digital experiences.

It’s close to impossible to survive in today’s market if you are not using any personalization strategies. However, traditional marketing strategies have a blinkered view of personalization.

A common misconception is that adding product recommendations at various stages of customer journey is enough. But in reality, product recommendations are just one of the many aspects of personalization.

So, in the hurry to get on the personalization bandwagon, companies end up using quick-deployment options such as product recommendation widgets and audience segmentation-based personalization tools.

An important factor affecting the executive decision of purchasing personalization engines and related platforms is the cost. An AI-powered personalization engine may be considered expensive against widget-based options that can be as cheap as $9 per widget per month. It becomes easier for business executives to justify poor ROI against low costs than to invest more and place higher trust in one software.

In addition, most large businesses also suffer from legacy systems and poor tech stack consolidation. Multiple tools are purchased at different points of time for different reasons, and the potential of each of these tools is not exploited fully.

A quick look at the tech stack of a leading US fashion brand (source: BuiltWith) shows that they use 4 marketing automation tools, 3 analytics tools, and 2 personalization tools. Instead of spending money on 9 platforms, if the business consolidates and optimizes its tech stack, they would be able to not only save on the software overhead but also achieve higher ROIs from the platforms they actually use.

Segmentation tools work on the simple principle of analyzing user data and putting each user in a segment with other users who display similar traits. These segments are based on demographics, technographics, interests and affinities, onsite behavior, relationship level, etc.

A single user can be in multiple segments—for example, a 42-year-old male interested in golf can be in 3 different segments (gender: male; age group: 36-45; income group: $100,000-$200,000, interests: golf).

At a simplistic level, this kind of segmentation works—there’s no denying that. So, when this individual comes to a website selling golfing accessories, they get targeted ads or offers based on their age group or gender or income level (the interest being irrelevant here as they are on a site of their interest already). However, when they visit a website selling t-shirts, their interest segment is also given equal weightage, and they may be shown t-shirts with golfing references first.

The above strategy can, of course, yield positive results in conversion rates in general. But what if the individual has no affinity to golf-reference t-shirts? What if they like to keep their sport interests separate from their fashion interests? That personal information cannot be understood by tools that simply use segmentation based on third-party data.

Take Monica, a 28-year-old looking for evening dresses. She searches for “evening dresses” on a brand website and clicks through to the product page of a classy purple dress. A possible segment she has been grouped into by the site’s personalization tool would be: “age: 25 to 34” + “search for evening dress”.

The recommendations Monica receives on the product page will probably be different types of evening dresses, based on the most popular ones on the site or from the same brand as the product she is looking at—and these recommendations wouldn’t be wrong. This strategy still has a good chance of converting the user into a customer.

But consider this: Monica’s favorite color is purple. In fact, she has shopped for purple attires and accessories from the same website a few times in the past. This changes the whole way the recommendations should have been personalized for her, doesn’t it?

Imagine a personalized recommendation panel of visually similar products reading: We know you love purple, so check out these evening dresses!. And then, to add more layers to the probability of conversion, a second set of “complete the look” recommendations containing complementary products and accessories, and a third recommendation panel with trending or popular products from the same category.

This deep personalization strategy with multi-pronged product recommendations and content will have a higher chance of helping Monica find the right dress (and even some accessories to go with the dress) than the previous segment-based mode.

An average personalization engine fails to process and integrate all data points available about an individual user, is dependent on segment rules, and is not capable of delivering the individualized experiences shoppers of today expect.

Individual-level data unification and 360-degree view of customers can only be achieved through ML-based platforms such as the ADA Global Personalization Suite. You need an algorithmic foundation and real-time analytical horsepower to provide 1:1 personalization that will facilitate better customer experiences and customer expectation management.

Read more about ADA Global Personalization Engine and how it can help your business.

This is part one of a three-part series on the importance of individualized customer experiences in eCommerce. The second part discusses why hyper-personalized customer experiences are the key to survival in eCommerce today.

It is no wonder that marketers have capitalized on social proof messaging software to persuade consumers to buy a product—with promotional lines such as ’99 per cent of people prefer’ or ‘highly recommended by’ to considerably increase the chances of a product being bought. Things are no different in the digital world. In fact, in the world of online and social media, the desire to be liked, accepted and fit in is also very strong.

Ever since the beginning of civilization, human beings have acted and behaved in a manner to be ‘liked’ or ‘accepted’ by peers and also are guided by influencers in society.

This concept of ‘social proof’ was first presented by psychologist Robert Cialdini in 1984 in his book, ‘Influence: Science and Practice.’ Social proof works best when we are indecisive about expected behaviour in a certain situation and look to other people to guide us towards action. Social proof is also effective when people around us are perceived to be well-informed and more familiar about a situation than us.

‘Pull of the crowd’ is also an important factor that influences our behaviour. For example, when we see people queuing up at a particular food kiosk, we are inclined to join this line, believing the food to be good.

This is best demonstrated by Apple’s social proof strategy showing long queues of eager customers waiting to pick up their new phone on the first day of its launch. All leading publications throng to click and publish the exciting moments before the stores open. Apple has managed to create a build up before the launch and make heroes out of consumers who manage to buy the product on Day One. This works as a great social proof marketing for its next product launch.

It is no wonder that marketers have capitalized on social proof to persuade consumers to buy a product—with promotional lines such as ‘99 per cent of people prefer’ or ‘highly recommended by’ to considerably increase the chances of a product being bought.

Also read: The Social Proof Advantage: Elevate Your Ecommerce Game

Social proof in the digital world

Things are no different in the digital world. In fact, in the world of online and social media, the desire to be liked, accepted and fit in is also very strong. Thus, people imitate or follow the actions of others because they believe it is the ‘correct’ behavior in a particular situation.

For instance, social media messages by Indian celebrity couple Anushka Sharma and Virat Kohli endorsing an ‘animal and pet friendly’ life have gone down well with young people. The couple regularly posts messages to show sympathy and concern towards stray animals.

Digital marketers are also using the power of collective influence to win over uncertain customers because they find comfort in large numbers. For instance, when the concept of UPI was introduced in India, there were doubts about its safety. But, in just two years, people are saying, “When a large number of people are using it, it must be safe” and are downloading online payment apps.

Marketing companies are using social proof to reinforce marketing messages and substantiating product/service claims through testimonials, user reviews and feedback, customer referrals and social media shares.

Whether it is choosing a laptop for work, a beauty product, a vacation rental, or any other product or service category, social community recommendations, referrals and customer ratings go a long way in improving real time customer engagement trust, leading to a successful purchase or decision.

Research by Nielson says that globally, around 92% of respondents, trust recommendations by friends and families. According to research by BrightLocal, in 2019, about 91% of consumers stated that positive reviews make them more likely to use a business. As many as 63% of customers are more likely to make a purchase from a site with user reviews. For example, every restaurant listed on food delivery app Zomato gets a rating by users. The higher the rating, the more likely that a new set of users will order from the restaurant.

New-age marketers are also using trust icons, data/numbers, influencer marketing, user generated content, and ‘featured in’ badges to boost social proof.

Benefits of social proof

• Authenticity: Authenticity is very important to consumers, especially millennials, when choosing brands they want to support. User generated content, like reviews, are seen as the most authentic form of content a brand can use to showcase their products.
• Trust: Social proof builds trust with the audience. For instance, company reviews by employees enhance trust in a company for a new prospect; in fact, further boosted if companies add a name and a face to the testimonial.
• Emotional connection: Social proof from other customers creates an instant emotional connect with potential shoppers. For example, if a person is looking to go on a pilgrimage, a carefully worded testimonial from a like-minded individual who has gone on a similar trip will strike the right chord.
• Click-through rate: Social proof can boost click-through rate by catching the attention of potential customers. By harnessing the power of social proof, marketers can convince people to purchase their product or service simply because other people are doing the same.
• Conversion rate: Social proof gives a huge boost to conversion rates. Research shows that great testimonials can increase conversion rates on sales pages by 34%. Online product reviews can result in an uplift of an impressive 270% (Spiegel Research Center). Using FOMO (fear of missing out) as a push method, marketers can improve conversions from anywhere between 40% and 200%.

If there is a lot of uncertainty and weak social proof, the impact could be negative. So, it is important for marketers and retailers to adopt high-quality social proof techniques to realize a huge conversion impact.

For ecommerce personalization brands, leveraging social proof messaging pushes people’s confidence in making purchase decisions and helps eliminate doubts before making the actual purchase. Marketers must first identify the uncertainties of their customers and buffer them accordingly with appropriate social proof measures.

Brands also need to bear in mind that social proof messaging solution is not always a quick fix for persuasion and conversion. And one method does not serve everyone. It’s important to assess and understand the audience well and choose a social proof type that works best for them. They must also use it subtly, cleverly and sparingly, so that they do not come across as being too pushy or too direct.

This article was first published on ETBrandEquity.com.

Use this checklist to find out.

Do you understand various search behaviors?

Like most retailers, you are seeing a greater influx of visitors – both new and old. Over the past 24 to 30 months, there has been an appreciable shift in shopper expectations, needs, and behaviors. So, is your commerce search keeping up?

To analyze this better, let’s take a step back to understand visitor behavior. Even as your visitors traverse through various stages of their customer journey, they exhibit different behaviors in how they use commerce search. For example, visitors who know what they want demonstrate a very different pattern of searching compared to those that are not sure what they are looking for and need assistance or even those that are looking for a solution to address a challenge but don’t know how.

1. Exact Search Terms – e.g., Configuration and model number of a gadget or a search such as ‘Sneakers made of recyclable material’ of a certain size and color from a particular brand.
2. Search with abbreviations – e.g., 15’ or 15 ft or 3 Ounces or oz
3. Misspelled words – Broccolli or Vaccum

4. Alternative words – Bed sheet or bed linen

   

5. Plurals – Sunglasses or Shades

6. Search terms based on a problem – ‘Dry skin’ instead of ‘body lotion’

   

   Are you able to home in on visitor intent?

7. Shopper searches can reveal high intent signals. If you can pick them, you can dramatically improve the relevancy of your search results for the shopper and prevent ‘no results’ situations.

   Some terms are more important, e.g., 12″ sky blue sleeve (where 12″ is as important as the color of the product), ‘Organic’, ‘Gluten free’, etc.

   

8. Your shoppers’ collective searches are revealing as well. They can help you determine associations between categories, products, and search terms. The best part is that algorithms that can self-learn can make these associations without you needing to write rules manually or change product data.

   

9. A shopper’s affinity for custom attributes like style, pattern, size, color, or preference around brands and products can be understood not just from their current session but their history as well. Personalizing search based on this information can drive higher conversions.

   What are you doing to aid visitors to explore the most relevant products in your catalog?

10. Sometimes, visitors are unsure as to what they want to purchase. This is your opportunity to guide, inspire, and even surprise them with meaningful yet interesting search results. This is what separates plain vanilla searches from personalized commerce searches.

    As shoppers begin typing into the search box, you could predict what they may be looking for and provide some inspiration.

    Increasing search result relevancy with each keystroke makes for better experiences.

    

11. When products need multiple dimensions for selection, contextual filters can help quickly narrow down results.

    

12. With 70% of traffic coming from mobiles and the limited real estate that these devices afford, you need to be able to personalize commerce searches. You want users to find what they are looking for faster. And that means your search needs to be contextual and display the most relevant items at the top of the results. We use a metric called Findability to measure if shoppers are finding what they need.

In other words, commerce search must be simple, intuitive yet sophisticated. It must reduce the need for users to scroll or use filters, and thereby help them complete their journey quicker.

The commerce search box is the perfect ally for marketers and merchandisers in helping deliver the best experience and boost conversions without relinquishing control over aspects like stock counts, trade promos, and private label growth metrics.

Commerce search can be really potent for retailers if it’s personalized. So, how would you rate your commerce search based on these 12 points?

Learn how you can move from legacy, one-size-fits-all search to self-learning personalized search.

It is not just enough to know if personalization is working on your commerce site. Apart from displaying the right content or products to shoppers, you should also know why and how they are being shown. But do you?

As marketers aim to drive higher visitor engagement, delivering personalized customer experiences has always been a top priority. The more individualized the experience, the higher the engagement and better the conversions. By investing in artificial intelligence and machine learning for e-merchandising teams, companies want every website visitor to have their own unique experience, rather than defining a generic, one-size-fits-all experience.

AI solutions these days, rather most of them, are made for automation. They are not designed to build on existing capabilities and the accumulated intelligence internal to the business. There is also a definite lack of visibility into how they arrived at an outcome. Such ‘black box’ solutions are quite simple, and thus do not provide business controls, visibility, and extensibility, thereby limiting the companies from uncovering the best outcomes using AI.

Xen AI, through the Experience Browser, opens this “black box” and gives you visibility into how the system works, giving you unprecedented control over areas to improve and explore, test new possibilities, and deliver the best outcomes.

The Experience Browser: AI Transparency the Way You Always Wanted

Understanding why and how an optimal experience was selected for an individual is key to aid decision-making for business users. That is exactly what XB, or Experience Browser provides – a keen insight into how Xen AI is driving performance.

The XB UI sits on top of the customers’ websites and gives you actionable insights into how dynamic personalization experiences are being created. Traditional tools do not even come close to having such functionality.

An intuititve visual overlay enables business users to edit and audit AI decisions using a single click on aspects like unified customer profile, summary of recommendations, dynamic segments, rules involved, and strategy evaluation.

Picking Individuals From the Unified Customer Profile

All the interactions that any specific individual has had with your brand can be viewed through the XB. Since all the data does not include personally identifiable information, whatever analysis you conduct and insights you derive don’t step upon the privacy of your customers.

All searches, the different dynamic segments they belong to, declared and derived preferences and affinities, clicks, prior purchases, and standard geo-location data if shared are available at a single click.

For companies that run omnichannel businesses, matched real-time customer profile with offline and store purchases can be depicted by the XB.

Understand the Strategies Used to Drive Decisions on Every Placement

Multiple areas in any website can be personalized. These areas include, but not limited to, product placements, content personalization platform, search overlays, and promotional offers. Through the XB, one can easily understand why and how the decisions were taken by the Experience Optimizer, the heart of the Xen AI engine.

In short, multiple strategies are measured against one another in a competitive match by the Experience Optimizer and the best one is chosen as per the context in real-time. After applying the merchandising rules, the one-to-one personalization experience is determined for every customer.

Along with the strategies picked by the Experience Optimizer and the rule selection, the Experience Browser also shows the reason behind that particular selection and a progressively filtered result set.

Real-time Trends on the Website

At any given point in time, you can use the Experience Browser to view the product trends as they happen, in real-time, including views, clicks, and purchases on the website. Holiday or seasonal sales preparedness can benefit greatly from this.

In terms of products viewed but not purchased, the insight gained can help merchandising teams determine boost versus bury rules.

Deep Links to Experience Insights

The presentation of real time customer data profiles reporting and multi-dimensional analytics provided by the XB nicely complements the ADA Global dashboard. Easily accessible reports for sales attribution, slice-and-dice by segments, cohorts and more across key metrics – attributable sales, revenue per visitor, session, etc. are just a sample of the insights that you can derive from the XB.

Content Performance Rankings And More

Visualizing dynamic content personalization performance at a glance is really handy. This is also made possible by XB. Drilling down to details like which pieces are being used where and how much is the performance affected by any kind of placement change. In a few clicks, you can determine engagement as well as conversion through the XB. Understanding how much revenue flowed in due to a creative asset is a unique capability offered by the XB.

Experience Browser Is Imperative for Your Holiday Readiness Plan

Having visibility across your strategies and their efficiency in driving personalized product recommendations is key to generating more conversions. That is exactly what the XB helps you achieve. Club that with your holiday readiness initiatives and you have a recipe for success.

More Reading:

Check out our Guide to Omnichannel Personalization to learn how to integrate your online and offline channels and deliver a unified experience to consumers.

The world of marketing has become way more defined and data-driven than it used to be. Multivariate tests and A/B tests, analytics, statistical significance, allocation of traffic, tweaking variables, refreshing, optimizing, and repeating – are now an integral part of every marketer’s life.

Clearly, testing has a huge bearing on the performance of campaigns and is the difference between running an optimized campaign versus a poor-performing one. Testing tools providers have built entire product lines around these, and you will find that they are constantly urging marketers to “test everything” and rightly so.

The world of retail is now too complex to tackle with manual testing. Manually tackling different flows of data for every individual is obviously not feasible. Adding more people to the testing team isn’t the answer. At the end of the day, the volume of data is too large and the value-add may be sub-optimal.

This begs the question: How can your business then provide tailored shopping experiences to customers without having to create discrete experiences manually for segments?

ADA Global steps in here with a range of native testing tools built specifically to address such concerns. These include traditional A/B and MVT tests, but in addition also have full-fledged continuous algorithmic testing and optimization.

What Is Algorithmic Testing?

Taking A/B testing and experimentation a notch or two higher, algorithmic testing uses data science with a continuous, always-on testing environment for improving conversions on the go.

It’s a critical need if your customer journey culminates with a purchase and you want to test conversions, especially on cart pages.

What Is Different About Algorithmic Testing?

As opposed to manual testing, algorithmic testing allows one to continuously test every variable, i.e., every digital transaction, while focusing on revenue conversion and at the same time, self-optimize to achieve the desired result via AI/machine learning, adapting at scale for every individual.

Algorithmic testing is one of the foundation stones of hyper-personalization.

How Algorithmic Testing Works

This works in two stages via an AI decisioning engine, known as the Experience Optimizer that is unique to ADA Global. While considering the context, the Experience Optimizer tests and picks the best experience for every individual automatically.

One can choose from over 150 strategies that have been tried, tested, and refined over time with outcomes specific to retail. ADA Global’s Xen AI consists of these strategies, and they are built using an ensemble of algorithms – including statistical, machine learning, and deep learning ones, like natural language processing or NLP.

In real-time, multiple strategies are tested against each other by the Experience Optimizer to determine the winner for any interaction. While doing that, it considers metrics like RPS, RPV, and CTR. It also tries out new content or other viable recommendation strategies that are relevant to each customer or visitor.

In its exploration mode, which is completely customizable by the business, the Experience Optimizer makes sure that results never hit any kind of ceiling, and continue to propagate over time.

New content or strategies can also be added into the mix by clients, and the Experience Optimizer can choose when they could be used given a situation.

ADA Global’s native A/B and MVT tools can be used by marketers to test out specific content, placements, strategies, rules, and even the merchandising of category and browse pages. These tests can then be targeted and accordingly, traffic can be allocated towards specific segments or all visitors. As opposed to traditional A/B testing tools, algorithmic tests treat each visitor uniquely.

Testing of strategies; optimization of metrics for different areas of the funnel; and testing of other configuration changes, campaigns, or promotions can also be done by clients.

The insights dashboard can be used to monitor results, or clients can even use the Experience Browser for the same. Results across multiple metrics are automatically shown, even if those metrics are not included by the user in their test. A variety of filters, including, but not limited to segments can be used to view the results. Outlier filtering can be used to measure the impact of outliers.

As the future of experimentation, algorithmic testing will operate alongside your regular A/B testing tools. It’s not like one can replace the other.

Learn about all the ways we can help your business drive hyper-personalized marketing at scale.

One of the most remarkable shifts we have witnessed in retail recently has to be the unprecedented growth in the grocery segment. According to a recent report from IGD, grocery is set to grow by 24% and generate an incremental $2.2 trillion in sales by 2024.

The key drivers for growth across both mature and emerging markets in North America/Europe and Asia respectively will still be online and convenience. As grocers race to grab a share of the increase in consumer spending, they must be mindful of the changes in consumer behavior, the importance of catering to a whole new set of first-time customers, and adapting quickly to evolving needs. This means grocers need a fresh and nimble AI-driven approach to delivering exceptional customer experiences. This alone will separate the winners from the also-rans.

For starters, this means grocers must look beyond traditional marketing methods that involve segmentation which is essentially grouping individuals into buckets called ‘segments’ based on a certain set of common predetermined criteria including interests, geographies, and demographics.

In the past, marketers would typically define segments such as ‘shoppers over the age of 45 who spend 40 GBP monthly in-store’ or ‘households that buy more than 10 organic items monthly’. This helped marketers better organize large amounts of consumer data and personalize communications to some extent. Clearly, it has its drawbacks – since every consumer is unique, no two shoppers have the same buying habits, tastes, or preferences and naturally, you end up with sub-optimal results.

Bottom line, it isn’t true Individualization or what we now call hyper-personalization – where every customer is delivered unique, tailored experiences. Make no mistake, consumers today expect grocers to deeply understand their needs, preferences, brand affinities, and purchase behavior in real-time and to engage them via their preferred channel.

In other words, consumers are looking for contextual relevance, and hyper-personalization delivers on that promise by creating a real-time behavioral profile for every visitor that is extensible to their household. This is achieved by a deep learning framework, a real-time customer data platform (CDP), a full suite of experience personalization, and personalized marketing orchestration capabilities that afford retail grocers the benefits of precision marketing and scale.

Clearly, hyper-personalization is the way forward, but let’s look at what this means from a consumer’s standpoint. In a nutshell, it translates into three things – Recognize Me, Understand Me, and Inspire Me in real-time.

Recognize Me: Know your shoppers. What if you knew your customer shops for a family with two children during the first week of every month, and 30% of his spend is on frozen food. A small portion of purchases includes vegan products, indicating a family member is a vegan. Legumes, dairy products, and dark chocolate bars are regular buys. The customer also buys vitamin supplements, fabric softeners, and disposable plates every alternate month. With this knowledge, you will be able to surface personalized product recommendations that drive better conversions.

Understand Me: Another important ingredient of hyper-personalization is knowing the Why behind the purchase. It can provide surprising insights into the shopper’s context and enable you to tailor the shopping experience. If you know that a customer often purchases avocados and is into healthy foods, you could recommend products that complement it (Mexican salsa for example).

A change in buying patterns or habits may be indicative of moving to a certain variety of food (gluten-free) or an addition of a pet. You may also discover that this customer engages with gamification content and often redeems points to get discounts or you may encounter a customer that constantly compares prices for select staples online and buys in-store. This level of deeper understanding allows you to adapt instantly to changes in customer behavior and extend the best, most relevant offers.

Inspire Me: The more you know your customers, the better your ability to personalize and drive conversions. When you understand affinities, preferences, and behaviors deeply, you can surprise and delight them with the best in-the-moment recommendations that drive higher engagement and top-class digital experiences. These can range from recipes to one-click ingredient purchases or recommendations based on the weather, unique bundled offers, or surprise customers with new product alternatives. You can bring insights from your in-store purchases to influence the online shopping experience.

In summary, massive opportunity beckons grocers. So why not look beyond the mundane automated replenishment to deliver a rich user experience that keeps customers coming back? Hyper-personalization has all the answers to convert routine buying into delightful shopping experiences. Make grocery shopping joyous again.

Check out our Guide to Omnichannel Personalization to learn how you can integrate your online and offline channels to deliver a more holistic customer experience.