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.