A deep dive into implementing AI-based analytics to help transform your product experience and build a strong brand loyalty
The World runs primarily on people’s emotions. Their perceptions influence their actions, which largely shapes reality around you. In this article, we’ll take a dive into a small subset of our world — retail.
You’ve heard the saying — Customer is King. As cliched as it sounds, it is a timeless one. If you’re into the retail business, your customer’s emotions and experience largely determine where and how they spend money — and it’s usually on things that make them feel good. So in theory, if you’re able to influence the experience of your customers, the more it does good to your business. The customer’s perception is your reality.
So it’s no wonder that in this age of internet, many e-commerce and retail companies pour billions of dollars into understanding the billion dollar question —
How do I make my customers happy?
An ageless question, with no easy answers. Committing to a brand has become harder than ever. Competition is fierce, and customers are faced with the paradox of choice. And retailers now, are faced with some hard challenges:
Challenge #1: Inconsistent Channel info
Keeping product catalogue and information constantly consistent and updated across all different channels is a major one. With so many channels to buy from, it is common for product information to get outdated or redundant, and customers could get overwhelmed and confused by redundant or irrelevant information.
Challenge #2: Redundant and Repetitive Processes
A lot of in-store and online operations, labor work, and customer engagement are repetitive in nature and difficult to scale beyond a threshold.
Challenge #3: Lack of market insight
Knowing where the market trends are heading towards is crucial for any business. This means having a historic data of purchase patterns and the ability to forecast sales in the near future. Do you know what your customers are really looking for?
Challenge #4: Scale
With so much of information pouring in, how do you scale up your operations?
Inevitably, retailers are turning towards technology as a means to improve the shopping experience and solve the problems that come with scale. Over the years, various kinds of software for e-commerce have emerged to handle mission critical processes like:
- Inventory Management
- Payment Processing
- Point-of-Sale, Invoicing/billing
- Marketing Management.
But now we have another problem — Information Silo.
A data silo is a management system in which one information system or subsystem is incapable of reciprocal operation with others that are, or should be, related. Many software solutions exist that aim to solve problems like inventory management, marketing or sales, but they don’t seem to work together.
There is a real need for omnichannel approaches. Omnichannel involves integrating and orchestration of multiple channels and software that retailers would commonly need. It makes life much easier this way, rather than the cumbersome and inefficient process of using single channels in isolation.
Recognizing existing problems and opportunities, we aim to build a standalone PIM (Product Information Management) system that:
- Focuses on product catalogue management that provides a full variety of features that is simple to use, well-visualized, and smart search.
- Has robust infrastructure for data import, transform, integration, and distribution.
- Is equipped with AI & ML to automate and streamline manual operations.
- Is cloud-based, to fulfill the customer’s need to quickly scale and pivot.
By 2021, experts predict over 50% of online retailers will choose a PIM solution to boost their sales and increase their efficiency.
Product Information Management (PIM)
To put it briefly, a PIM is a multichannel marketing software tool that allows real-time tracking for all data contained in a company’s catalogues and inventory by sending it appropriately to all channels, and ensuring every channel connects with each other.
Over the years, e-commerce has witnessed earthquake changes that call for innovative solutions:
Omnichannel information consistency and precision has become life-or-death.
- 50% of consumers have reported sending a product back because it didn’t match its description;
- 59% of consumers expect a company to have consistent information on every channel;
- When content does change, 60% expect a company’s website(s), emails, and distribution channels to all reflect these changes on the very same day.
Smart product content personalization with AI has become the game. Play it or lose it.
- 79% of consumers will only engage with personalized offers
- Increasing personalization across channels can boost consumer spending by as much as 500%
- Shoppers who click on personalized recommendations are 70% more likely to make a purchase.
Automation and streamlining in product information management can save huge costs.
- Businesses employing PIM automation improve up to 25%, annually.
- 25 min per SKU per year is spent on manual item data cleaning, which would take only 4 min with automatic synchronization.
- Before the introduction of a PIM system, less than 47% of staff at retailers spend less than 2h per week searching for products. After implementing PIM, this percentage grew to 58%.
Cross-border shopping is booming.
- > 57% consumers make purchase from other countries
- 92% consumers prefer to shop in local currency
- 59% consumers rare or never use English-only websites.
Let’s explore further —
Key capabilities of a PIM system
- Organize/Centralize: From a company’s ERP, where data files are stored (flat files, excel sheets etc.) including other sources of product information like resources of images and videos, a PIM system collects all such material and channels it in the right directions.
It is responsible for searching elements; to manage the translation of descriptions; to classify categories; to correct errors; to edit updates and changes; to enrich the product description, and to synchronize their presence in all areas.
2. Analyze/Optimize: On top of organization, PIM can also provide you with Gap Analysis and quality report of your data.
3. Connect/Distribute: Through PIM, members with access to the system can organize and classify products according to the categories and labels desired, and update any associated data, such as prices, product features or images that will appear equally on all platforms linked to the PIM system.
Inconsistencies will never occur again between physical and online catalogues, or between products in the same collection that have different data sheets.
Now with this intro, let’s look into the intelligence that drives some of these features—
The role of AI and ML in a Product Information Management System
Machine Learning can be used to automate and make many of the manual processes much smarter in a PIM. Here are a few applications:
Application #1: Backend Catalogue Data Processing
We can start by streamlining and Automating manual process of importing product data with AI.
Technology behind the scenes:
Python has a robust ecosystem and open source libraries that help with data imports, extraction and manipulation.
For extracting product information, we can use python-tesseract — an Optical Character Recognition (OCR) tool for python. That is, it will recognize and “read” the text embedded in images.
Python-tesseract is a wrapper for Google’s Tesseract-OCR Engine. It can easily read all image types supported by the Pillow and Leptonica imaging libraries, which mainly includes jpg, png, gif, bmp and tiff etc.
The main functionalities that it supports are —
- AI to identify raw data, either CSV, EXL, PDF, images, to extract the data piece needed.
- Auto-populate essential product data (name, title, description, etc.)
- Automatically standardize data imported to be a uniform format
- Visualized drag-n-drop/linked graph (or any other methods around visualization), for the user to understand how the data is transformed, and make any changes (e.g. ProductsUp)
Let’s understand with an example —
Application #2: Front-end catalogue management
Here are a few features you could build:
- AI-powered search function to get rid of complicated hierarchy and categories (text auto-completion, key word associations, etc.)
And here’s how you could build such a feature using AWS QuickSight — a Scalable, serverless, embeddable, machine-learning powered BI Service for the cloud:
Transform data and create dashboards simply using AWS Glue DataBrew and Amazon QuickSight
Before you can create visuals and dashboards that convey useful information, you need to transform and prepare the…itcareersholland.nl
- Web-scraping existing competing products before new product launch, providing competitor insights.
Check out How Web Scraping Crawler Lambda Function can be implemented using AWS AI and ML services
Gathering Market Intelligence from the Web Using Cloud-Based AI and ML Techniques | Amazon Web…
Many organizations face the challenge of gathering market intelligence on new product and platform announcements made…aws.amazon.com
Application #3: Performance analytics
Predictive modeling of past trends of demand, for marketing and sale purposes. Time-series analysis and forecasting methods are the most commonly used approaches.
Technology behind the scenes:
Here’s a sample ode for implementing Statsmodels, ARIMA and Facebook Prophet in Python —
Using the sample code, you’ll understand how to implement the following features:
- Link to inventory management for inventory tracking and real-time product availability (SKUs)
- ROI strategy recommended by analyzing poor-sold and better-sold products
Application #4: BI dashboard to show product selling performance
Now coming to the visualizations, “plotly graph_objects” has been used to build the BI dashboard.
Why is Plotly a great exploratory tool for data scientists?
A major advantage of using Plotly is that it encourages us to be as creative as possible with your visualization since any complex plots only use three main concepts: data, layout, and figure objects. As an engineer, this is probably one of the best things to have as we can build and rebuild graphs in any way we want. Here’s an article that explores this in greater detail.
Check out some examples of the kind of visualizations that you could build:
Hopefully you’ve understood what Product Information Management is all about. But not everything is about information! Establishing a PIM system in your business is the beginning of a larger goal, known as —
Product Experience Management (PXM)
It’s necessary to understand that this is not the same thing as PIM. PIM (Product Information Management) system is the foundation for PXM. Product Information Management software provides all product content and remains a crucial element and enabler of the practice of Product Experience Management, focusing on the content aspect of the product experience.
In other words,
PIM system is WHAT business use to describe products and services, but PXM is HOW you stage an experience. — Brandquad.io
5 ways to reinvent your conversion strategy with Experience Driven Commerce
#1: Automated Content Planning and Creation
Machine learning can perform beyond simple content creation; it can enable marketers to better use that material for more impact.For example, ML tools can analyze both competitor strategies and user behavior to determine the best approach to engage with potential customers.
Here’s how you can build a simple content planning and creation algorithm using Amazon Personalize:
#2: Smarter Product Content Personalization
The value of machine learning lies in the ability to create opportunities from data, and it’s already changing the way marketers manage their campaigns. From producing trillions of dynamic ad variations to formatting them across platforms in milliseconds, machine learning frees up creative staff to focus on creative ideas and strategy.
Dive Deeper : How to build it with Amazon AI Service — Amazon Personalize
Amazon Personalize can now create up to 50% better recommendations for fast changing catalogs of…
Amazon Personalize now makes it easier to create personalized recommendations for fast-changing catalogs of books…aws.amazon.com
#3: Conversational Commerce
Conversational commerce or trade is a system of direct communication between a brand or business and its customers, through instant digital messages that are shared on platforms such as WhatsApp, Facebook Messenger, iMessage, Viber or Alexa de Amazon, and that incorporate the ability to make purchases online. — pimnews.orgFor example H&M chatbot gives customers advice by analyzing the preferred choices of their looks. According to this, chatbot picks other matching stuff to describe their personality, favorite style, and so on.
Dive Deeper : How to build it with Amazon AI Service — Amazon Lex
#4: Augmented Reality
There are several emerging technologies that can help enhance customers’ experiences, such as 3D, Augmented Reality (AR), and Virtual Reality (VR). These immersive solutions are like super-powers for your product information. They can be used to create a more rich, realistic, and appealing buying experience. In addition, they increase effectiveness as 3D interactive content drives 94% increase in page views and helps reduce costly returns.
A better customer experience with a higher conversion rate and fewer returns means more revenue.
Dive Deeper : How to build it with Amazon AI Service — Amazon Sumerian
Market Growth of PIM
MarketsandMarkets estimates the global PIM market size is expected to grow from USD 9.0 billion in 2020 to USD 16.0 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 12.2% the forecast period. The major factors driving the growth of the PIM industry include rising demand for PIM solution from flourishing eCommerce industry and increasing need to offering enhanced customer services.
Product Experience Management is crucial to customer experience. Every single interaction that you have with your customers is an opportunity to create something truly remarkable. The product information that you provide is an integral part of the customer journey. The quality of your product data influences the trust in your brand, your products, and ultimately the customer’s buying decision.
What’s the expected business impact?
- Customers switching companies due to poor service costs U.S. companies a total of $1.6 trillion.
- Companies with a customer experience mindset drive revenue 4–8% higher than the rest of their industries.
- 84% of companies that work to improve their customer experience report an increase in their revenue.
- 22% of Fortune 100 companies have a C-level customer officer, compared to 10% of Fortune 500 and 6% of Fortune 1000.
If you’re interested in integrating AI and ML in your existing business solutions, give a shout to us on social media or shoot us an email!
References and Sources:
An End-to-End Project on Time Series Analysis and Forecasting with Python
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and…towardsdatascience.com
The New Beginnings of AI-Powered Web Data Gathering Solutions
Are you approaching data gathering on a large scale in a traditional manner? If so, expect to invest a lot of time and…towardsdatascience.com
Optimizing Product Content for Voice Search | Agility Multichannel
Science fiction has been promising us the convenience of voice interaction with computers for decades. Smart devices…www.agilitymultichannel.com