Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(44)

Solar Industry(8)

User Experience(67)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

10 Chatbot Strategies eCommerce Brands Use to Boost Sales In 2023

By :

Online shopping isn’t just about silent category browsing. It is about customer communication first. Hearing and in-time guiding customers at each step of their journey is key to sales growth. 

Sounds like a task for a 24/7 customer service team, heh? It’s a good thing that a chatbot tool for business can automate part of these processes. 

Moreover, 40% of shoppers are ready to use it. Tommy Hilfiger is one of the many brands that use that knowledge. Its chatbot brings the brand an 87% rate of returning customers. Another case is the Just Eat chatbot, with a 266% conversion rate.

Intrigued? There are more examples in the article! Find out ten chatbot strategies eCommerce brands use to convert customers on websites, messengers, and social media 👇

24/7 assistance on FAQs 

Imagine a never-sleeping support manager answering repeating customer queries around the clock, with no vacation or coffee break. 

It is an automated chatbot. Think about such an employee when building your customer service 😉

Launch it and:

  • Provide visitors with instant self-service at any time. 
  • Save budget by focusing managers’ time on solving high-priority issues.

Example from the Hitee chat👇

In addition to simple questions, this FAQ chatbot can provide customers with information about insurance options.

Notify consumers about new products

This case is popular in fashion and luxury retail. Instead of mainstream emails, they talk about new collections in messengers. And for a reason! For instance, compared to the 25% Open Rate of email, Facebook has an impressive 80%.  

Thus, when the new collection is live, its subscribers see +1 in DMs. It is a company chatbot telling customers about new items in stock. Casually and cheerfully, it engages them to browse for more pieces directly in a messenger without switching to a website. 

Example from Burberry👇

This luxury retail brand implemented a Facebook Messenger chatbot to introduce customers to their latest collection of bags.

A chatbot by Burberry on MessengerImage source.

Recommend products

The ability to generate endless chatbot ideas makes it an ideal tool for businesses. And this scenario is a good confirmation of that. Launch a chatbot that will define customers’ preferences in an up to five-question dialog. 

Examples of product recommendations from Lego👇

The company launched Ralph the Gift Bot to help its customers choose the perfect gift: 

Process orders 

Allowing customers to order in a chatbot is a great idea to save your managers time and follow the introverts’ desire to avoid direct communication. 

Here is how it works. Customers choose an item and place an order without leaving a chat. For this, people share personal details like name, telephone number, and billing address, and a chatbot will route them to the checkout page on a company website. 

An example from the 1-800-Flowers store

In addition to the gift choice, its users can also submit their order information. A chatbot is like your inbound lead conversion administrator who collects recipients’ addresses, names, and phone numbers, billing addresses and only then routes them to a website checkout page.

Finally, the best thing here – to make the customer experience better, the chatbot offers to save this data.

Tell about sales and promotions

Enhance your sales campaign with a proactive chatbot message. Choose a segment you want to send it to and launch a personalized offer, for instance, 20% off on a new dress collection for customers who visit relevant store categories. 

As for the conversation scenarios, there are two options:

  • Showing products on sale and routing to a checkout or shopping cart.
  • Offer personalized recommendations of items on sale.
  • Capturing customers’ emails in exchange for a coupon.

Here is an example of how it can work 👇

This chatbot engages customers with a bright image, and then shares coupon codes.

Recover shopping carts

70% of online buyers leave items in their carts instead of buying. The fix?

  • Launch a website chatbot to engage visitors when they are trying to leave.
  • Launch a messenger or social media chatbot to re-engage those who left. 

E-commerce marketers switch to this strategy because of the low Open Rate of the classic follow-up emails and the high price of the SMS channel. 

An example of a cart-recovering chatbot 👇

Perfuel Pet Suppliers sends follow-ups in a Facebook Messenger chatbot for registered customers who left the store without a purchase. 


Image source

Upsell and cross-sell

Depending on the product page customers visit, or their shopping cart, a chatbot can suggest additional products or upgrades.

Here is an example of how it works on Shopify👇

When a customer is on a particular product page like jeans, in some time a chatbot message appears “I see you eyeing our new black Levis jeans..” and offers to discover matching items.

Gobot eCommerce Chatbot
Gobot eCommerce Chatbot

It is a great example of how businesses transform customer experience and personalize it. 

Help customers track orders

In a short conversation, a chatbot will define the issue, capture the order number, and share its status instantly. All you have to do is to integrate it with the logistics system. 

Order tracking chatbot example👇

MR.DIY, a Malaysia-based home improvement retailer, launched such a chatbot for its website visitors. In real-time, the chatbot delivers information on where is a customer’s order: 

It brought MR D.I.Y an 80% growth in its containment rate. 

Collect customers’ feedback

There are several challenges that e-commerce businesses face when trying to gather customer feedback:

  • A low response rate of the marketers’ attempts to get customers’ feedback via email.
  • Customers post negative feedback on socials or review websites.
  • A lot of time is spent collecting, managing, and analyzing customer feedback. 

The fix? Automate the process with a chatbot.

For example, contact them on checkout after the payment or after a conversation with a customer manager. 

For example 👇

You can send a short survey with stars and a comment field or turn the process into a conversation by reacting to the rating the customer gave you.

Image source.Image source.

Engage customers in the loyalty program

Use a chatbot to automate the way you:

  • Engage customers to join your loyalty program.
  • Register them.
  • Provide loyalty points updates.
  • Suggest rewards they can redeem.
  • Answer FAQs.

Loyalty program chatbot examples 👇

The first case is about loyalty program registration. The chatbot collects customers’ contacts and promises to notify them about discounts.

Image source.

The second is about points updates and announcements. It actually does what the first promised – send loyalty program updates and engage to continue shopping.

To sum up

Inspiring examples, right? When correctly set up, chatbots provide personalized interactions, resolve queries swiftly, and bring you an army of loyal customers. But to make any examples work in your business, mind the following rule – segment and personalize its workflow with the info about customers’ behavior and preferences. 

About the Author: Evelina Carillo is a friendly and skilled writer and blogger with more than a decade of experience in crafting all sorts of content for the marketing and business world. She’s also spent five years diving into the exciting world of EdTech, where she’s continued to learn and grow in her field.

Cancel

Knowledge thats worth delivered in your inbox

Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

By :

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot