Boost E-commerce Growth and Brand Reputation Using Customer Feedback Data
Updated: Feb 5, 2021
Once only leveraged by enormous corporations, big data today has become an indispensable driving force for growth and operations in small and medium businesses. Easy access to big data technologies and the growth of analytical tools has spurted out opportunities for harnessing a variety of data-based insights not thought of before. Customer feedback data is being used for e-commerce growth and for improving brand reputation.
By continuously tracking various forms of online consumer data,, brands are now able to combine online consumer footprints with intelligent analytics tools to make off of their brand perception and improve online brand reputation by using data to intelligently influence buyers. Remember how your Uber app would offer you discounts in a specific city the moment it learns through your location data.
Studies find that for an enterprise, customer analytics are essential for improving customer experiences and hence brand reputation across all marketing, sales and service channels. Recently, a study conducted by Harvard Business Review Analytic Services in collaboration with SAS, Intel and Accenture Applied Intelligence suggests the following statistics:
70% of enterprises have raised their expenditure on real-time customer analytics solutions over the past year.
By using customer analytics 58% of enterprises are witnessing a significant increase in customer retention and loyalty.
60% of enterprises use real-time customer analytics to improve customer experience across touch points and devices.
44% of enterprises are gaining new customers and increasing revenue as a result of adopting and integrating customer analytics into their operations.
These statistics suggest that global enterprises are rapidly embracing the role of customer analytics in improving their business operation and create a future roadmap for their company.
Customer Reviews influence Purchasing Decisions
Today, reviews hold the greatest power in a buyer’s purchasing decision making. Whether it purchasing a car or a new mobile phone, renting Airbnbs or getting cab services, or even from finding a diner to hunting the right tour for an expedition, users go through reviews before making any expenditures.
Research suggests that nearly 92% of e-commerce buyers look into the reviews of a product online before making a purchasing decision. Moreover, 72% of all buyers irrespective of online or offline shopping, still browse through reviews online before making a purchase in a physical store. The statistical evidence suggests that reviews and ratings largely affects a buyer’s purchasing instincts, and the presence of negative reviews on a product affects the brand equity of a company negatively.
The average star rating of a product is one of the most intuitive indicators of the overall quality of the product for the consumer. Even if a product has a large number of reviews written online, a typical consumer may/may not have the time to go through all of them. In most cases, the average star rating serves as a quick summary of how past buyers have perceived the brand.
Moreover, the average star rating determines how highly the product shows up in e-commerce organic search results. This also improves the product’s SEO standing in overall Google search results.
Therefore, to improve their online reputation and brand equity, brands need to not only strive to raise the average star ratings of their products but also the rating of the overall brand. This improves consumer perception, leads to a higher level of trust leading up to faster sales conversions. This raises your e-commerce search rankings and in turn improving your online visibility.
Real-time monitoring of your product’s online reviews helps you identify the larger pain points and act on it.
Review Analytics: The Ultimate Tool to multiply Sales and Revenue
Once extracted and analysed well, review data can work wonders for you. Freetext.ai’s Text Analysis tool not only enables brands to look at their own products but also monitor how competitor’s products are doing compared to theirs.
Three actionable ways to use review data for improving sales and revenue generation are:
1. Transform your marketing strategies
When it comes to devising new marketing strategies, online review data can act as a fuel for streamlining your strategy. Listening to what people have to say can help you focus on the customer’s pain points and widen your customer base. According to a recent survey on consumer reviews, nearly 85% of consumers rely on online reviews as much as personal recommendations.
Image courtesy: HARVARD BUSINESS REVIEW ANALYTIC SERVICES REAL-TIME ANALYTICS: THE KEY TO UNLOCKING CUSTOMER INSIGHTS & DRIVING THE CUSTOMER EXPERIENCE
Marketers can use this data backed decision making to come up with better marketing prospects. Acting on negative reviews, brands can identify trends causing the negative reviews and show improvement. Thereby winning back customers. After all, framing your rectification as “Here’s what we’ve changed after hearing you out” makes for a good marketing comeback.
Review data collected demographically and analysed can reveal in which demographic markets your product is performing well and in which aren’t. Data suggesting underperformance in a regional store can prompt you to reconsider your branding techniques and launch a different marketing campaign there to improve sales.
2. Enhanced decision making
Your review data can enable you to make better decisions for your business. This data will help you identify the positive and negative trends surrounding your product, get a sense of the impact created by your product, and help you create a clearer roadmap for the future of your company.
Integration of this data will not only enable you to streamline your marketing strategies but also help you formulate major business decisions.
3. Improved Customer Service
Reviews essentially pinpoint your customer’s issues with your brand and put your revenue and sales targets at risk. Responding immediately to negative reviews can make or break your business.
Once negative review data is gathered, find out the major areas in your product or service where consumers are facing challenges.
Give immediate response to show them you’re trying your best to remedy.
Use this as an opportunity to gain back the distraught customer’s trust
Responding to your customer’s negative feedback the right way can not only make your turn a negative experience into a positive one but also gain you positive testimonials useful for your future marketing efforts.
How Datahut and Freetext.ai can help you get the most out of Review data?
Datahut and Freetext.ai have collaborated to provide companies with enhanced review data analytics. With Datahut’s expertise at large scale web data extraction coupled with Freetext.ai’s AI analytics tool for extracting sentiment analysis, organizations will now be able to achieve an in-depth insight into their product/brand’s perception online.
Most organizations track essential KPIs like Net Promoter Score (NPS), Customer Satisfaction Scores (CSAT), and Average Star Ratings. While these KPIs give a general idea about a brand’s overall performance and customer satisfaction, they are not enough to delve deep into customer pain points.
Using Datahut’s review data feeds and Freetext.ai’s AI-powered data analysis tools, brands will now be able to track:
1. Average Star Ratings over a period of time
When shopping on an e-commerce portal, buyers are tempted to check out the product’s star rating. While an average star is an intuitive indicator of a product’s perception by its buyers, the average star rating available online are ratings at the snapshot level i.e rating of a product at that point of time. A product with a low current average star rating may have started off as a high rated product. Thus, realizing what caused the product to underperform over a period of time is essential to build remedial development strategies.
2. Track Sentiment Score per area of focus
Sentiment Analysis is a subfield of Natural Language Processing (NLP) that creates a system to identify and extract opinions from within the text. Besides identifying the opinion, these systems extract attributes like:
Polarity: if the writer expresses a positive or negative opinion,
Subject: the topic of discussion,
Opinion holder: the person, or entity that expresses the opinion.
Often times, brands focus on tracking an overall sentiment score for their products. However, an overall sentiment score is not an accurate indicator of a product’s brand equity. For example, an XYZ speaker is a top recommended item on e-commerce stores. However, it’s sales have been dwindling over the past few months. While the speaker may be offering competitive features in its category, there are many factors which may attribute to the negative online image of the speaker. These can be sound quality, packaging, delivery time, battery life, wireless connectivity, returns etc.
Sentiment analysis over an area of focus enables brands to identify pain points in their product’s perception at a granular level and take remedial action accordingly.
3. Product/Brand’s rank in the category
Review data of your product and your competitors can be analysed to assess your product’s overall standing/rank in its category. Categorizing similar polarity reviews can even help brands assess in which aspects their product performance lags behind their competitor’s. For example, a brand’s speaker may be a top performing model in terms of sound quality but may garner negative reviews for shorter battery life than its counterparts. Accounting for such lackings timely can help brands gain a competitive advantage.
Datahut and Freetext.ai are here to bring you deeper and actionable insights from your brand’s online review data. Wish to leverage review analytics with us? To know more, contact Datahut.