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Sentiment analysis answers one vital question – “What is the consumer saying?

A study suggests that 57% of consumers buy a product after reading reviews across e-commerce sentiment portals. According to our research positive reviews and ratings uplift sales by at least 18-20%. Alternatively, bad reviews can cause a substantial loss.

Consumers share their sentiments about the product/ service through these reviews and ratings. The Sentiment is essentially a consumer’s trust/confidence in a brand that influences their buying behavior.

It is easy when a brand has a countable number of reviews as it is easy to read and assimilate the findings. Unfortunately, popular brands have reviews and ratings running into thousands, which is difficult to process. That is exactly where Sentiment Analysis comes into play.

Cataloging emotions through customer reviews, a.k.a., sentiment analysis, plays a vital role in this field.

Essentially, it is helping brands to gather scattered consumer opinions into positive, negative, and neutral categories. It uses an AI to monitor the textual content of reviews and categorize consumer opinions. Sentiment analysis is a technique through which brands can work on their product offering, understand customer expectations, fine-tune customer service, increase sales, and fine-tune their marketing strategy.

In today’s competitive world, delivering an exceptional customer experience accredited with sentiments has become important. A Gartner report states that “customer experience is a primary objective of 89% of companies. Sentiment analysis helps in narrowing down on issues faced by the brand, which helps brand owners make decisions targeted and faster. So, it is the best tool to monitor reputation, performance, and customer experience.

3 Benefits of Sentiment Analysis

● Word Cloud – Sentiment Score & Analysis

Reviewing sentiments through the word cloud helps brands understand the areas of improvement and the most appreciated features. It helps the brand identify the exact consumer emotions based on themes like quality, health, delivery, etc. The outcome of an analysis like this helps brands make specific decisions to improve their ratings and reviews under a defined theme. Generic reviews or analyses are inconclusive in nature.

Keeping track of Sentiments month-on-month helps the brand owners measure and map their own performance vis-à-vis competition. The Sentiment score helps in understanding how favorable or unfavorable a brand is in comparison to the median average in its category. Hence, the score can be used across e-commerce platforms to understand a Brand’s overall customer acceptability.

Categorizing reviews by positive, neutral, and negative, helps a brand owner get a real-time understanding of customer responsiveness towards the brand. The monthly aggregated results are a brand’s guide to navigating the e-commerce landscape.

● Valuable Intelligence to Drive Market Strategies

Sentiment analysis data helps a brand evaluate its standing against its competition. It helps understand consumer expectations and improve customer engagement. Additionally, it also helps in identifying problems and taking corrective actions in a shorter turnaround time.

The valuable intelligence drawn over a period of time by analyzing the sentiments helps the brand custodian formulate targeted marketing strategies to entice its customer base. This is the foundation for creating a hyper-personalized experience for their customers.

● Future Planning & Overall Competitive Edge

Sentiment analysis helps understand the consumer pulse. It is one of the fastest ways of understanding consumer needs and expectations. As a result, it paves the way for new product development and gives them an overall idea about customer reactions.

The sentiments can also be used to improve the customer journey in real time. Faster response time builds a loyal customer base. Gauging and comparing the brands and their competitor’s performance via sentiment analysis gives one an edge over the competition. This can be used as a real-time feedback mechanism to change consumer perception towards being more positive.

Takeaway

mScanIt powered by mFilterIt has cutting-edge technology that consolidates reviews and ratings across the platform on a single dashboard for easy access and action. It summarizes all positive, negative, and neutral sentiments of the brand along with its competition and equips the brand custodian to make a decision in real time.

eCom Competitive Analytics helps in deep diving into sentiments/opinions and drawing actionable insights to improve overall performance in the eCommerce landscape. Therefore, it simplifies the complexity of the customer journey by aiding decision-makers with measurable data at various stages of the purchase funnel.

70% of shoppers admit that the price of the product influences their buying decisions across categories. Another study says a prospective buyer will visit 3 or more sites to be assured of the pricing competencies before making the buying decision. Trigger-happy customers are also price-sensitive customers. If 60% of the decision-making process is purely a price-play, brands should take this into cognizance and start treading this landscape meticulously.

Determining the right product price is vital for e-commerce brands, as consumers’ buying behavior alters significantly through it. Most consumers compare similar products on the same or different e-commerce stores before making the final buying decision. Their decisions are influenced by promotions, offers, referrals, preferred retailers, etc.

Some consumers might even try to reach out to a brick-and-mortar store and compare the price with online stores for buying products at the lowest price.

Pricing intelligence is a technique retailers use to stay ahead of the competition. It helps brands monitor and analyze factors like consumer behavior, price trends, and response to price changes. These factors enable brands to develop pricing strategies, boost sales, grow market share and consumer base, and increase revenue.

Because of the dynamic nature of e-commerce as a business, pricing intelligence becomes one of the vital tools to combat competition and increase revenues.

Research reveals that Amazon changes product prices almost every 3rd minute on average to better sales.

4 Important KPIs for Developing Pricing Intelligence

● Gauging Average Price Vs. Competition

The overall average price determines the estimated SKU price of a brand compared to its competition. In other words, it helps to understand the “price market share” of a brand versus its competitors. Additionally, brands get an idea of the top “price performers” based on the total sold units.

The Average Selling Price (ASP) is a good place for brands to start their price benchmarking and take a clear stand on their pricing strategies. Pricing higher than the category ASP can classify a brand as premium and pricing lower can make it a mass category. These decisions impact the marketing and advertising strategies and decide the brand’s future.

mFilterIt’s e-commerce Competitive Analysis determines ASPs across pin codes, locations, platforms, sub-categories, SKU-wise, sub-brand-wise, and variants. Using this information, brands can increase/decrease the production of a variant, alter investments in the marketing budget, or increase/decrease distribution in a specific region.

● Average Price Per SKU Unit

The average price per SKU is the price per unit (gm/ ml etc.) for a product vs. its competitors in the same category. A brand needs to look closely at this data and correlate it to its performance.

The average price per SKU unit helps to understand the market dynamics. It gives a brand a fair idea about how their different SKUs perform on the pricing aspect and helps them understand the customer’s point of view and how price-sensitive the market is.

Accordingly, they can decide the type of pricing that works in the market and understand pricing trends.

● Average Price – Platform Wise

The estimated price of a brand’s product on different eCommerce stores is called platform-wise average price.

Reviewing the ASP across eCommerce stores helps brands find the best platform for boosting their marketing investments and acquiring higher revenue. Moreover, brands can keep a check on competitor prices accordingly and monitor unauthorized reseller prices.

mFilterIt’s eCommerce Competitive Analytics helps analyze the ASPs across platforms and allows a brand to maintain its competitive stand in the overall market.

● Average Price by Variant

Price by variant allows the brand to monitor the price of a particular product variant across different platforms and understand performance. They can compare their variants’ pricing vs. the competition’s variant and monitor their performance.

mFilterIt eCommerce Competitive Analytics also divides it into brands, sub-brands, sub-categories, and variants. The prices can offer an in-depth review of the highest and lowest-performing products across platforms.

Moreover, brands can compare the average price of similar variants and draw insights into their pricing strategies. Similarly, brands can acquire a price-based performance overview for their sub-brand categories.

Conclusion

eCommerce pricing intelligence is no longer an option for online retailers. Pricing delves deep into a consumer’s mind and helps a business understand and survive the crowded landscape.

A business’s survival has a lot to depend on the value its customers derive from their brand, the incredible market forces, and the competition that follows it.

Pricing as a component is a complex piece of a puzzle. There are no straight answers anywhere. Experience and continuous experimentation are required to deliver the correct consumer value. mScanIt helps brands to dive deeper into the journey by providing results based on real-time data.

Brands using the mScanIt pricing tool are no longer required to conduct secondary research for determining pricing trends. Moreover, brands get a single-page price-based overview that helps optimize product pricing across platforms, categories, sub-categories, and variants.

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