How E-Commerce Companies Can Leverage AI & ML Technologies To Ensure Business Growth

Artificial Intelligence in marketing

Gone are the days where marketers were shooting in the dark with their campaigns and hoping for success. And, gone are the days where marketers solely rely on inefficient survey data to understand their target audience and their competitors. In today’s scenario, technologies such as Artificial intelligence (AI) and Machine Learning (ML) are at the forefront of every innovation. These technologies predominantly help in the transformation of an enormous volume of data into insights that allows better decision making. Before going further, it is essential to have an overview of AI and ML.

Artificial Intelligence in marketing

Source: https://sloanreview.mit.edu/wp-content/uploads/2019/02/GEN-Kardon-AI-B2B-Marketing-1200x627-1200x627.jpg

What is Artificial Intelligence?

Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.

 

What is Machine Learning?

Machine Learning is a subset of AI, which enables machines to learn from past data or experiences without being explicitly programmed.

 The following Infographics by Smart Insights, the creators of the RACE Planning Framework, outlines the various possible applications of AI & ML across the customer lifecycle.

RACE Planning framework

The following are the five areas in which the AI & ML technologies are extensively and actively being used in e-commerce improving marketing and business outcomes.

Generating Customer and Competitor Insights

 

Collecting and analyzing customer data from various sources allows us to discover insights that help in precisely recommending relevant products or services, thus increasing sales and customer experience. Text-mining customer reviews from e-commerce sites, social media, blogs, and review websites, and applying sentiment analysis algorithms provide a precise perception in which the customers view the products/services. These valuable insights help e-commerce companies in product optimization and filling customer experience gaps. Additionally, this analysis also assists in unearthing upcoming market trends and helps in staying ahead of the competitors.

Customer Segmentation

Customer segmentation is the method of grouping customers of similar characteristics, and it’s one of the cornerstones of effective marketing. Segmentation traditionally was done on the basis of age, gender, and other demographic parameters only. Though this helped marketers in dividing the customers into larger generic groups, it failed to help them understand the customers deeply. Implementing AI/ML in customer segmentation removes these limitations by leveraging customer behavioral data in creating more targeted segments that improves the efficiency of the marketing initiatives. Utilizing AI/ML in segmentation is not only efficient and scalable but also removes the human bias that’s involved in the traditional process.

Customer Feedback Loop

Source: https://thefinancialbrand.com/wp-content/uploads/2019/10/micro-segmentation-flow.jpgngs/2019/02/GEN-Kardon-AI-B2B-Marketing-1200x627-1200x627.jpg

Automated Content Generation

One of the factors that determine the success of the marketing campaign is the content that’s creative, appropriate, and importantly must resonate with the target audience. Yet another challenge in creating compelling content is understanding of the subject matter and industry dynamics. Marketers shall leverage Natural Language Generation and AI technology in overcoming these challenges. AI has evolved to perform keyword research autonomously and deliver content that aligns with the campaign objectives. NLG used along with Natural Language Processing (NLP), enables automated response to reviews and customer queries in e-commerce and social media platforms, thus saving on the resource cause and improving customer engagement.

AI with NLP model

Source: http://ceur-ws.org/Vol-2196/BPM_2018_paper_21.pdf

Campaign Optimization

Reaching out to the right customers with the right content at the right time defines the success of the campaign. Nevertheless, customers also demand hyper-personalized communications and offer that resonates with them. As a result, it has become a mounting challenge for marketers to adjust and optimize their content in real-time. Taking advantage of AI-powered analytical optimization helps marketers in meaningfully utilize every penny of the marketing budget in acquiring customers. The AI algorithms are capable of accessing the market, competitors SOV and layers the same with campaign objectives in intelligently placing the Ads for better CTR and conversion.

Demand Forecasting

Demand forecasting prevents e-commerce companies from having an excessive amount of goods in stock or out-of-stock. It uses predictive analytics, a statistical technique used in Machine Learning, to precisely forecast customer demand by analyzing historical data. These forecasting insights help e-commerce companies reduce supply chain costs and optimize financial planning, capacity planning, and risk assessment decisions. Marketers shall leverage demand forecasting by incentivizing customers with offers to buy products/services during a low demand period and plan resources during high demand to sustain customer experience. One of the common pitfalls is that it would be hard to predict the demand accurately for new products. However, using data from products of similar categories helps to overcome this challenge.

Demand forecast and analysis

Source: https://d3e3a9wpte0df0.cloudfront.net/wp-content/uploads/2019/11/Fashion-retailers-image-11.jpg

 

SetuServ’s Customer Review Insights and Signal (CRIS) platform employs state of art AI and ML algorithms specifically modelled for the e-commerce industry that enables text mining from various sources and helps in delivering insights and content recommendations leading to business development.

The Key Advantages Of Mining Customer Reviews

text mining software

The Key Advantages Of Mining Customer Reviews

 

Technology is breaking down the entry barriers for new brands especially in terms of distribution and marketing, aided by factors such as smartphone usage and internet penetration. Subsequently, the evolution of e-commerce has resulted in extreme disruption of the retail industry over the past decade. In a short span, E-commerce went from being the disruptor channel to the only available option for many product categories, due to its increased adoption. In fact, recent research from Grand View Research estimates the current E-commerce global market to be USD 9.09 trillion and expected to grow by a CAGR of 14.7% from 2020 to 2027. 

The indisputable fact is that superior customer experience are the critical factors that helped sustain this tremendous growth. With mobile, internet, and other technological advances ensuring ease of purchase, both the e-commerce companies and the sellers have started focusing extensively on improving the customer experience to keep their business afloat and stay ahead of their competitors. And, customer reviews are invaluable in providing meaningful insights. In addition, customer reviews greatly impact purchase decisions, as 97% of the online customers read reviews before buying a product online. The following are the three richest public data sources for mining and analyzing customer reviews for business growth. Together, these sources can 

  1. Identify influential factors to product satisfaction
  2. Isolate customer experience pain points 
  3. Track consumer response to competition.

Text Mining

Mining Customer Reviews

Before identifying the source to mine the customer reviews, it’s essential to narrow down which customer review channel works best for the objective of the analysis. The raw customer reviews data can be extracted from the following sources individually or as a combination of sources based on the business goals.

 

E-commerce sites:

Most of the e-commerce platforms, including famous ones such as Amazon, eBay & Flipkart, enable and encourage their customers to rate and post a review on the products that they have purchased. This source is the most reliable customer review data that’s mined online.

 

Social Media:

In recent years, many customers have started expressing their opinions about products and services on social media. It has also become a channel where brands are actively engaging with their customers, making it an emerging channel in mining customer reviews.

 

Review websites and Online Forums: 

Review sites, niche blogs, and online forums can be excellent sources of reviews of a particular product segment. At times, many customers prefer voicing their concerns on review sites and forums, rather than posting on social media.

The raw textual data are mined from the above sources using distributed crawling techniques and pre-processed by using Stop words Removal and Normalization techniques before using Natural Language Processing (NLP), Machine Learning, and Sentiment Analysis Algorithms to extract meaningful insights.

 

Advantages Of Customer Review Mining

The insights derived by mining customer reviews assist the business growth and in improving the customer experience with the following aspects.

 

Identify Influential Factors:

Customer Review insights would help in understanding the factors, features, and attributes influencing the buying decision and that the customers really care. These insights assist the business in better product placement among the target segment, thus leading to better business outcomes.

Optimize Customer Experience 

The customer reviews not only help us understand the sentiment of customers towards the product and services but also helps in understanding the underlying product issues and service gaps leading to those sentiments. Improving these shortfalls helps in sales growth and in generating additional positive reviews.

Track Competition

It’s quite usual for the customers to compare the product with its competitor in the same segment while posting reviews. Analysis of these reviews would provide the precise shortfall of the product or service respective to its close competitors. This analysis also helps in understanding the dynamics of the market segment, thus contributing to valuable insights holistically on the ecosystem for better decision making.

 

SetuServ’s Customer Review Insights and Signal (CRIS) platform is capable of tracking millions of data points from multiple sources, and analyzing the same using AI models with custom taxonomies based on the market segment. This analysis helps businesses in precisely understanding the market landscape and purchase drivers influencing customer decisions.

Importance of Understanding Consumer Emotions since COVID-19

corona virus word cloud

People’s lives across the globe have been disrupted and impacted in unprecedented ways due to the current COVID-19 pandemic situation. The spread of this pandemic has meddled with the lives, livelihoods, communities, and businesses worldwide. The Asian Development Bank (ADB) has forecasted that the COVID-19 induced economic loss could go up to $8.8 Trillion, i.e., 9.7% of global GDP. The International Labour Organization (ILO) has pointed out that more than 400 million full-time jobs have been wiped out globally due to this pandemic.

corona virus word cloud

Source: https://medium.com/@TheDataArtist/creating-wordcloud-from-twitter-feed-using-r-370f509275af

This pandemic has also brought in a unique set of social and economic challenges that have emphasized the actual value of identifying a business’s strengths and weaknesses, as well as recognizing and mitigating associated risks. At this critical juncture, organizations around the world have pushed themselves in innovating solutions to limit the economic disruptions and put them back in the path of recovery. In order to do that, it’s critical for the organizations to empathize with their customers at the emotional level. In this post, we shall discuss the importance and ways of understanding customer emotions during these times.

 

Importance Understanding Customer Emotions

With most of the customers restricted within their homes due to the COVID-19 lockdown, delivering experiences and services that meet their new needs with empathy and care would be the influencing factor in the survival of the business during this pandemic. The companies that are capable of delivering the same would create a long-lasting positive customer experience thus leading to better customer retention and sales. 

Currently, the key challenge for marketers is to get a pulse on the customer preferences in real-time and rapidly innovate to optimize the product and identify the customer journeys of the target segment. In order to achieve the same, along with agility and responsiveness, organizations must also ensure that their decisions are backed through data and real-time research.

 

Ways to Understand Customer Emotions

The traditional marketing channels have gone obsolete during this pandemic, and the marketers have had to rely solely on  digital channels for  insights to influence their target audience. The following are the sources that can be leveraged to efficiently understand the customer’s emotions.

 

  1.     Search Queries

Google, the leading search engine that processes around 70,000 searches every second and 5.8 billion searches every day. All these searches are customers trying to find solutions for their problems. That is a lot of data that can be leveraged in driving business results. A systematic keyword research shall help in understanding the following.

  • Who is your Target Audience
  • What are their Areas of Interest
  • What are their Needs
  • What is their state of mind

 

Analyzing the search queries provides insights on the customer’s emotions and needs and adds more value to the business.

 

  1.     Social Sentiment

Social media has become a prominent digital platform where customers voice  both their concerns and satisfaction about the product or service. Text mining the social media posts of the target audience and performing sentiment analysis on the same  provides valuable insights about their current emotions. These relevant insights help in identifying product/service gaps and anticipated features that can be funneled to improve the customer experience leading to better business growth.

 

Setuserv’s Customer Review Insights and Signal (CRIS) platform can help businesses in improving their marketing content reflecting customer’s emotions and needs based on the automated search query analysis, and also perform sentiment analysis that helps in a deep understanding of their customers and market ecosystem. Read more about the CRIS platform here.

Role of Sentiment Analysis in Market Research

Social Media Sentiment Analysis

Technology is radically transforming the customer journey, making customers more connected and more empowered than ever before. With more than 59% of the world’s population connected via the Internet, customers are now actively voicing out their opinions on social media and other online platforms, leading to an enormous volume of user-generated content. For brands, these immense data provide deep insights into consumer behavioral trends and help companies learn about customer perceptions on their products, services, or events. Here is precisely where techniques such as Sentiment Analysis plays a pivotal role in Market Research.

What is Sentiment Analysis?

Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and bio-metrics to systematically identify, extract, quantify, and study effective states and subjective information.

In a nutshell, Sentiment Analysis is a text analysis technique that enables interpretation and classification of human emotion and intent within the text data.

Social Media Sentiment Analysis

Source: https://theprauthority.com/

Need For Sentiment Analysis

Most of the Market Research focuses on quantitative metrics such as market share, market penetration, and SOV. However, there is a gap in performing qualitative analysis that helps in understanding the real pulse of the customers. The real challenge causing this gap is most of the customer-generated data such as social media posts, chat conversations, blog posts, and emails are unstructured data. This type of data is not only hard to analyze and understand but also time and resource consuming.

Sentiment Analysis employs text mining and Natural Language Processing algorithms that analyze unstructured data and derives insights that help businesses in deeply understanding their customers and competitors.

Sentiment Analysis Methodology

Below is the process flow of a generic Sentiment Analysis process

sentiment analysis process flow

  1.     Data collection

As the Sentiment Analysis leverages vast user-generated content, the raw data from various data sources such as Social Media, Blogs, Discussion Boards, Review Sites, and E-Commerce are collected and stored in a Data Lake. At this point, the data is highly disorganized and unstructured.

  1.     Text preparation

This phase involves the cleaning of the extracted data and prepares it for further analysis. For efficient analysis, irrelevant information such as noise, non-contextual content, metadata, and stop words gets identified and removed.

 

  1.     Sentiment Detection

Before leading to the next phase, it’s essential to filter the data further for efficient analysis. In this process, all the textual sentences in the dataset get tested for subjectivity. Only the sentences with subjective expressions are retained in the data set for further analysis, whereas the rest are discarded.

 

  1.     Sentiment Classification

In this phase, the subjective text data gets classified into various sentiment classifications. After sentiment classification, the polarity of the sentiment is detected and determined whether the text expresses positive, negative, or neutral emotion. Broadly, there are three types of sentiment classification algorithms.

  • ML-Based: This method uses Machine Learning algorithms, which leverages prediction models extensively trained using pre-existing labelled data.
  • Lexicon-Based: This approach uses a dictionary of words in which each word gets mapped with its emotional polarity and sentiment strength. Then, the dictionary is matched with the data to calculate the overall polarity score.
  • Hybrid: This method leverages the best of both above. It uses Lexicon-Based algorithms for training the prediction model, where Lexicon is proven to be more efficient. The same prediction models are used to analyze the data using ML for a quicker turnaround.
  1. Analysis Presentation

Conventionally, the analysis insights were published as standalone reports and graphs. But with the growth in technology to process a large amount of data in real-time, interactive dashboards with detailed data visualization features are currently used.

Sentiment Analysis Classification

Application of Sentiment Analysis in Market Research

Sentiment Analysis not only helps businesses monitor their customers’ perception of their brand but also provides valuable market intelligence on the complete ecosystem. The following are some of the secondary market research use cases of Sentiment Analysis.

 

  •       Competitor Analysis 

Sentiment Analysis not only provides insights about your online reputation, but  also  allows for  an understanding of what customers in the target segment think of your competitors.These deep insights help in bridging the gaps in products and services to resonate with the target audience and also aid business in staying ahead of the competition.

 

  •       Product Optimization

With the development of deep-dive analysis algorithms, it is possible to understand not only the sentiment of the customers towards a product but also the factors, features, and attributes.. Such insights help businesses optimize their offerings and make it attractive to their target segment.

 

  •       Brand Monitoring

Brand monitoring is one of the most common applications of Sentiment Analysis. It helps in assessing how a brand, product, or organization is perceived by the public, especially in  target segments.

 

  •       Customer Experience

An immense amount of customer data such as chat transcripts, voice recordings and e-mails get generated while customers interact with customer support. Measuring the customers’ reactions in these interactions would give us a clear picture of their level of satisfaction, and also reflects the performance of the support teams.

  

SetuServ’s Customer Review Insights and Signal (CRIS) platform can help businesses perform efficient Sentiment Analysis that helps in a deep understanding of their customers, marker ecosystem, and help stay ahead of the competition. Read more about the CRIS platform here.

Scaling up your Share of Voice in a Competitive Market

BDI and CDI

Share of Voice (SOV) is a critical metric in evaluating the overall marketing efforts as it reflects both marketing efficiency and product performance. In order to efficiently gauge brand visibility, it is vital to understand this metric and leverage the same to gain an edge over your competitors.

What is Share of Voice?

SOV is a brand’s share of the total spending for the category for a specific time period. In short, it’s the measure of the market the brand owns compared with its competitor. The below is the generic share of voice formula

Share of voice

Though there is a relationship between both metrics, SOV is often confused with Share of Market (SOM). To clarify, SOM is the brand’s share in the total sales in that category in a given time period, whereas SOV is the brand’s share of resonance in that category in the same period.

Share of voice by brand

 

Why is Share of Voice Important for the business?

SOV has always been an important metric for marketers as it helps them in assessing the visibility of their brand in the marketing place. Traditionally, SOV echoed the voice of the brands and their ability to gain media exposure. But, with the increase in social media penetration and customers sharing their viewpoints on it, SOV has become the voice of the customers that brands must perceive.

 The following are the reasons why SOV is critical to the brand & business:

 Acts as a barometer to the reputation and visibility of a brand

  • Measures the success of the marketing campaigns
  • Helps in strategizing and optimizing marketing spends
  • Helps in understanding the market and the competitor landscape
  • Provides insights into the customers, competitors, industry trends and issues

How to increase Share of Voice?

If the  objective is to improve the performance and market share of your brand, it is essential to scale up the SOV.

  1.   Know Your Share of Voice

The first step forward is to accurately determine the SOV, not only at the brand level but also on the Segment, Category, and Product levels. As it may sound simple, it requires a robust tool with lexical text analysis and Machine Learning algorithms that mines the following data from Social Media, Search Engines, Competitor, Review, and E-Commerce websites and transforms into meaningful insights.

 

  • Brand Mentions
  • Hashtags
  • E-Commerce Listings
  • Customer Reviews
  • Ad Impressions
  • Search Results

Accurate SOV not only helps to understand the brand’s position in the market landscape but also assists in better understanding the competitors and device further strategies in winning the market.

  1.   Leverage Allied Metrics

SOV used in conjunction with Brand Development Index (BDI) and Category Development Index (CDI) can help marketers focus on markets that have potential and optimize their marketing spends. Growth in both BDI and CDI eventually contributes to an increase in SOV.

 

The Brand Development Index (BDI) quantifies how well a brand performs in a market, compared with its average performance among all markets. That is, it measures the relative sales strength of a brand within a specific market [Source: Wikipedia]

 The Category Development Index (CDI) measures the sales performance of a category of goods or services in a specific group, compared with its average performance among all consumers. By definition, CDI measures the sales strength of a particular product category within a specific market. [Source: Wikipedia]

BDI and CDI

Source: https://braindispenser.wordpress.com/2011/02/20/how-to-use-bdi-and-cdi-for-planning-updated/

Go With The Trend

It’s pivotal for a brand to keep up with the ever changing social trends. The best way to stay afloat as a brand is by understanding the content that’s consumed by the Target Audience. By tracking the flash trends in the market and by creating content with trending keywords and phrases shall contribute to brand awareness, thus boosting the SOV.

Influencer Marketing

Partnering with the right influencer in the niche can help to build trust in the brand. Influencers also help in creating compelling content that connects well with the Target Audience and boosts brand awareness. Knowing your influencers and the right way to connect with them and their followers is key in boosting SOV.

  

The Market Landscape module of the Setuserv’s Customer Review Insights and Signal (CRIS) platform can help brands understand their effective SOV and industry trends and help them boost their market share and stay ahead of their competition. Read more about the CRIS platform here.

 

 

5 Ways in which Customer Sentiment Analysis can Improve Your Customer Experience

customer journey

Customer Experience (CX) has become an indispensable factor in running businesses today, with more than two-thirds of the companies primarily focusing on CX to stay ahead of their competition. Also, recent research by PwC shows that 86% of adult customers are ready to spend more for better customer experience, and this ground reality makes enterprises prioritize CX.

Getting Started – Data is the key

The best way to start is to focus on collecting customer data and feedback from multiple channels and leverage the same in deriving valuable and accurate insights. Then, use it in improving the acquisition, satisfaction, and retention of the customers, thus increasing the overall revenue. The more the number of customer touch points, the more data is generated and the generated data can be analyzed for superior actionable insights. Each stage of the customer journey has multiple touch points and it is important to have a great user experience across these stages. For example, in the awareness stage the ease of participating in NPS surveys, discussion forums, chats on the website etc becomes key to good quality customer experience. Likewise streamlining premium user experience across other stages like purchase, usage, customer support and loyalty rewards the brands with higher customer affinity if the brands proactively use the data to gain deeper insights.

customer journey

Customer Sentiment Analysis

Customer Sentiment Analysis is an automated technique that empowers us in the emotional understanding of how customers feel about a product, brand, or service. This process leverages advanced Natural Languages Processing (NLP) algorithms in ingesting a large number of online communications from multiple channels and converting them into meaningful insights that help in optimizing various use cases.

Below are the five ways in which Customer Sentiment Analysis would help in enhancing the Customer Experience

Understand Social Sentiment

In today’s context, Social Media is the go-to place for customers to bounce their feedback, either be a shout-out or a concern regarding a product or service. By continuously monitoring the brand mentions in social media, and streaming them into text mining algorithms, it is possible to build an overall sentiment index to measure the customer perception and reputation of the brand/product in the social spaces. The sentiment index is then further clustered into topic-wise sentiment index increasing the granularity of the analysis.

Improve Products and Services

Performing sentiment analysis on the product reviews on e-commerce sites and market places, surveys, support tickets, and transcripts of focus group interviews assists in identifying bugs and gaps between product features and customer expectations. This data guides us to strategize a stellar product road-map and service improvements that help improve customer experience.

Enhance Customer Service

The efficiency of customer service is one of the crucial factors determining customer experiences. There are multiple channels such as e-mail, chat, and calls where the customers interact with customer service to solve the problems related to the offered product/services. By running, sentiment analysis on the customer and agent interaction uncovers gaps in customer service, thus improving the overall customer experience.

Knowing The Competitors

A comprehensive competitor analysis aids in identifying influential competitors and provides valuable insights into their product/services and marketing strategies. By also running sentiment analysis on the competitors would benefit in understanding market gaps and opportunities that provide you an edge in selling your product. This analysis also improves the customer experience by learning from the mistakes of the competitor.

Optimizing Marketing Efforts

Sentiment analysis also acts as a guide to accurately derive the campaign performance by analyzing how customers react and engage with marketing campaigns. These insights also support in segmenting customers and deliver hyper-personalized messaging that improves conversion rate and customer experience.

 

SetuServ’s AI powered Consumer Reviews Insights and Signals (CRIS) platform is a proven solution when it comes to Customer Sentiment Analysis and helps you to provide a better customer experience to your customers.

 

How Deep Dive Insights Help Organizations Stay Ahead Of Competition

Obtaining insightful information about markets and competitors and transforming such data into valuable insights continues to be a challenge for most organizations today. Nevertheless, a majority of organizations rely solely on surveys, experience and intuition when making strategic decisions. As industries become increasingly complex and organizations are overwhelmed with data from a spectrum of digital channels, they must leverage both broad based data sources as well as Deep Dive Analysis to stay ahead of their competition. The deeper and more accurate the understanding of the market, customers, ecosystems, and competitors, the more the products and services shall be precisely designed to appeal to the target market and outweigh the competition.

 

Shortfalls Of Survey

Conventionally, businesses use surveys, most often with close-ended questions, as a primary research tool to acquire market intelligence. While surveys can efficiently target specific questions, attitudes and products to reveal details about specific areas, they fall short in uncovering the organic, emotional responses from consumers to a product or service. The following are the shortfalls in solely relying on surveys in acquiring competitive intelligence:

  • Surveys depend on the consumer’s motivation, honesty, memory, and ability to respond, and they are very susceptible to bias.
  • Respondents may not feel comfortable providing answers that unfavorably present themselves.
  • Questions are pre-selected and may drive a respondent to provide opinion where they may have been once indifferent
  • Inaccuracies in data due to discrepancies between the respondent’s stated opinions and their actual experiences and opinions.
  • Structured surveys with closed-ended questions may have low validity when researching effective variables.
  • Data aggregated from limited response shall not be generalized for the whole Target Audience group, as the number of respondents who choose to respond to a survey may be different from those who didn’t, thus creating bias.

 

Bridging The Gap With CRIS Deep Dive Analytics Module

With the continued growth in digital consumption and online competitive influence, businesses need to consider how to evolve to meet the changing demands of both the consumers and competition by moving beyond the limited scope of surveys. SetuServ’s Deep Dive Analysis module of Customer Review Insights and Signal (CRIS) provides a turn-key solution to uncovering organic consumer market research and competitive intelligence.

Deep Dive is a real-time analytics module (part of CRIS) that mines customer and competitor information from a variety of sources such as social media, review websites, and competitor websites and can also incorporate a company’s internal data sources such as e-mails and open ended survey results. Insights views showcase how a category, brand and/or product is trending, what themes are most impactful to performance and how these product feedback themes compare vs. competition Deep Dive employs a hybrid system of Machine Learning and Contextual Text Mining algorithms and augments the same with human curation in delivering competitive intelligence not just at the brand level, but drilled down to Category, Sub-Category, Product and Feature levels.

Listed below are some of the real-time insights that Deep Dive module is capable of delivering in a more granular level via an interactive and dynamic Tableau Dashboards.

Brand Performance

  • Fundamental emotion that a customer associate with the brand and the competitors
  • The sentiment of the brand against the competitors
  • Bench-marking new products and innovation of brand and the competitors
  • Attributes of the brand influencing purchase decisions

Segment Performance

  • Top selling products in the segment compared to competitors in the same category
  •  Attributes influencing customers buying decision in the segment
  • Opportunities for innovation in the product/service category

SKU/Product Performance

  • Overall customer rating of the product against its competitors
  • Market share analysis of competitors by product/service
  • Customer sentiment attributed to product features

 

SetuServ delivers bespoke deep-dive insights into the consumer decision-making process based on the specific needs of the organization and helps brands gain an edge against competitors. Learn more about Deep Dive here.

How To Leverage Machine Learning And AI For Market Landscape Analysis

Market landscape

Market landscape involves the broad mapping of various players in a specific industry, segment or geography with characteristics like business health, drawbacks and opportunities. Market landscape analysis helps to understand competition and create broad business strategies using specific business insights. 

 

Why and when to use market landscape analysis?

Market landscape analysis gives a wide canvas for brands, so the boundaries have to be defined and purpose driven. For example, it involves the mapping of existing players in a segment, competitor’s strengths and weaknesses, demographics targeted etc. The availability of tons of data helps to understand where a brand stands vis-à-vis competitors.

With such an analysis, making data driven decisions which are in sync with business objectives and emerging trends becomes easy. It allows organizations to build holistic go to market strategies or leverage on gaps left unexplored by competitors.  

The process of mapping various characteristics is useful when considering significant expansion or planning for diversification from core business. Market landscape analysis helps in building a superior strategic plan apart from playing a key part of setting up a collective impact collaboration.

 

Traditional approach to market landscape analysis

Traditional approach to market landscape analysis involves a combination of qualitative and quantitative techniques to understand size of the market, needs of the consumer and competition in the market. 

Qualitative research involves ethnographic study, focus group discussions, in-depth interviews etc whereas, quantitative research involves survey and statistical analysis of secondary data. Such a research project is considered to be time consuming and the sample size plays an important role in defining the accuracy of resultant insights.

The time consumed by similar research projects are very high thus, the chances of missing newer trends with rapidly changing technological landscape might arise. To mitigate such unforeseen risks, we at Setuserv have created a platform which helps in being dynamic with your market landscape analysis, the platform is called CRIS. It uses customized machine learning algorithms as per requirement of a brand to ensure actionable insights are mined.

 

CRIS Platform: Dynamic approach to market landscape analysis

The market landscape module of CRIS platform augments traditional approach of analyzing data with a specialized machine learning algorithm. The algorithm helps analyze data at granular level and gives easy to understand information about:

 

  • Share of Voice
  • Ratings of specific brands and their products
  • Customer sentiments
  • Trending Brands

 

Market landscape

 

The above diagram depicts the data from the health, household and wellness segment. 

Advantages of CRIS platform

CRIS platform is robust and allows to capture accurate insights by mining data about new entrants without dislodging existing information. Gathering similar data helps in effective competitive bench-marking.

The platform’s true potential is in ability to take more structures of data, this helps it to be scalable. i.e. The brands have their own proprietary data can be interlace with existing data being mined by the platform thus, creating highly customized and brand specific solutions. 

By combining the specificity of human intelligence which understands the emotional aspects of consumers with data mined from the abundant unstructured data using AI algorithms you have a strategy which is validated using data. This approach helps increase the confidence in actions taken as the insights of the human brain are validated through data from artificial intelligence.

Adopting the Amazon way of creating bestsellers through actionable insights

We often tend to romanticize the stories about intense rivalries like Steve Jobs vs Bill gates, Arnold vs Stallone, Messi vs Ronaldo etc and if the rivalry has a bit of underdog story similar to that of David vs Goliath then the interest levels peak. Well, when we correspond the same to various brands and their fierce competition among each other, there are few aspects which pop-up evidently. Every successful brand builds efficiencies in particular aspects of their business and rework on them continuously to gain more insights. For some businesses these insights might be intuitive but in this age of information overload and clutter of data, extracting insights which are actionable involves building complex analytical models and queries.

 

Gathering business insights the traditional way

Traditional approach of getting business insights involved a combination of focused market research campaigns, brand study and intuitive hypothesis basis limited customer interaction. Such an approach might not elicit the actual emotional thought process and expectations from a consumer which in turn can be used to create efficiencies in various processes or innovate new products. This approach lacks the flexibility for the current age as the trends, customer preferences and economic climate changes happen at a rapid pace. Adding these data points to the research increases the complexity and at times the analysis might become obsolete by the time the report is published.

For example, assume that you were launching a shopping website optimized for Internet Explorer and you had authorized a study on the same. While the study is underway, the consumers start shifting to Google Chrome and Mozilla Firefox. The change happens in a rapid and stealth manner that you as a firm might have an optimized website for Internet Explorer but the greater business opportunity would be lying with Chrome and Firefox. It would be double whammy of sorts as the study would be redundant and first mover advantage too is lost.    

 

Gaining deep customer insights

 Deep customer insights is a granular and intimate shared understanding of customer psyche which involves deep inspection of the spoken and latent, current and future needs. Such depth may not be possible through traditional methods especially market research through surveys and questionnaires as the number of data points to be recorded would be very high and customer fatigue would creep in while answering long questionnaires. The other factor is that traditional methods can’t be implemented at scale unlike building analytics models or data capturing infrastructure while mining deep insights. Creating a framework where data can be used for decision making will help small and medium enterprises to create superior products with better resource allocation and for large scale companies it helps optimize their marketing and R&D budgets. 

Doing things the amazon way

Amazon’s advantage is scale not only with product sales, but also product feedback on what sells. The company uses data on its portal to develop and launch private labels for products with high sales. The information available with amazon helps them understand how to price a product, at what margins they need to operate, features which can be included and whether the segment is a viable business opportunity. According to an article in wall street journal, Amazon used data about a niche category of best sellers sold by a third-party vendor like car trunk organizer. The information accessed included total sales which gives an idea of the opportunity, marketing and shipping spends and fee charged as commission. The data was used to introduce Amazon’s own car-trunk organizers thus helping Amazon achieve higher margins when compared with third-party retailers. Most retailers introduce their own brands using data to some extent but the information they have is far less than what is available to Amazon. This allows Amazon to be more dynamic in terms of pricing the product as per current economic conditions. 

 

Can the Amazon data mining model be replicated?

It might not be possible to replicate the process in its entirety as Amazon has access to proprietary data and the amount of information available is vast. However, we can use publicly available data on Amazon and augment this feedback with other public and private sources to build a robust solution. This is precisely what Setuserv’s Consumer Reviews Insights and Signals (CRIS) platform offers. The platform mines consumer feedback from multiple online sources including eCommerce platforms (this includes Amazon bestsellers data), business listing sites, social media portals, travel sites and all other major sources for reviews. The platform uses machine learning to mine through millions of data points and serve up insights. The two biggest advantages of the platform are 1) it has the ability to integrate data and insights across channels, both public and private and 2) it is fully customizable per domain, brand, company and/or business solution. The ability to customize helps in avoiding redundancy and aides in scaling up the solution for future use and integration to business operations.

Existing solutions vs CRIS

Setuserv’s CRIS platform is modeled on the basis of granular insights to uncover the WHY on consumer’s behavior to purchase. Granular insights, and the ability to benchmark against competition separates CRIS from existing platforms. In simple terms, AI models can be customized as per client’s data and use cases, thus, helping deliver insights which are actionable. 

As popularity for CRIS grows, so do the ready made available live data portals. Two such industry specific solutions are:

Sneakpeek: Insights offered by CRIS solution

CRIS solution mines millions of consumer review data points via multiple channels and integrates them to serve up market research insights. The comprehensive approach taken by CRIS platform while mining data gives the user an holistic understanding through three key modules:

  • Market Landscape 
  • Deep Dive Analysis 
  • Flash Trends 

CBD Insights From Public Comments to FDA

FDA recently asked for public comments on Cannabis-related products with the intent to understand public perception on the effectiveness of CBD products and to gauge public interest in legalizing CBD. This request received ~4,000 comments from April to July. We analyzed this data to quantify the public perception on CBD.

Overall, the commenters were very bullish on CBD. Their sentiment on CBD products was quite high as shown in the following graph.

 

This high sentiment was driven mostly by topics on “Advocacy for CBD Legalization” and a variety of effectiveness related topics as shown in the following graph. There is an ongoing debate on the legality of CBD. In this source, commenters have predominantly argued for making CBD legal.

 

“Pain Relief” which was the second biggest positive topic has a broad range of sub-topics under it, people are using it for all the following use cases –

 

Under “Preferred To Other Medicines/ Drugs”, below are the common products for which CBD is used as a substitute

 

Comments were written by both males and females, with males writing more comments than females, as shown in the graph below: