In this article, you will discover how we use AI text analysis to unlock valuable shopper insights and change the way our clients understand customer preferences and behaviours. By the end, you will understand how we go from thousands of text-based product reviews to something as simple and useful as this:
Creating actionable shopper insights has always been crucial for businesses looking to get an advantage in consumer goods. By analysing the text content of shopper reviews using AI technology, consumer goods businesses can gain valuable insights into customer preferences and behaviours. AI (Large Language Model) text analysis allows businesses to go beyond simple quantitative data and understand the emotions, opinions, and sentiments expressed by shoppers in their reviews.
Introduction
AI text analysis works by collecting and analysing large amounts of data from shopper product reviews. This data includes not only the text of the reviews, but also metadata such as the date, location, and rating of the review. AI algorithms then process this data to identify patterns, trends, and correlations that may not be immediately apparent to human analysts.
By revealing customer preferences through AI text analysis, businesses can identify trends and patterns that can inform their decision-making processes. For example, they may discover that customers highly value a particular product feature mentioned frequently in positive reviews. This insight can help businesses prioritise investments in product development and marketing strategies.
Finally, leveraging shopper insights for business growth is a key benefit of AI text analysis. For example, they can optimise their product offerings, improve customer service, and target their marketing efforts more effectively.
How AI text analysis unlocks shopper insights
AI text analysis involves four major steps, from data collection to gaining insights:
- Collect a large amount of shopper review data. This data can be obtained from various sources, retailer sites, social media, or customer feedback surveys. It is important to collect a large diverse range of reviews to ensure the most representative sample.
- The data is preprocessed to prepare it for analysis. This may involve cleaning the data by removing irrelevant or duplicate reviews, as well as standardising the format of the text. The data is then transformed into a format that can be processed by AI algorithms.
- AI algorithms are applied to the preprocessed data to analyse the text content. These algorithms can identify patterns, sentiments, and other linguistic features in the reviews. They may use natural language processing, machine learning, or deep learning techniques to extract meaningful insights from the text.
- We use human insights experts to sift the output for fresh useful insights. These insights can be used to understand customer preferences, identify areas for improvement, and any recent trends or hot topics. Visualisation tools and dashboards to aid interpretation and to present the insights in a clear and actionable format.
AI text analysis is a powerful tool that can transform raw shopper review data into valuable insights but only by combining the power of AI with the experience of an expert.
Identifying trends and patterns
One of the key benefits of AI text analysis is its ability to unveil customer preferences by identifying trends and patterns in shopper reviews. By analyzing the text content of reviews, businesses can gain insights into what customers like and dislike about their products or services.
For example, AI text analysis can reveal that customers highly value a certain product feature mentioned frequently in positive reviews. This insight can help businesses prioritize investments in product development and marketing strategies. They can focus on enhancing the features that customers appreciate the most, leading to improved customer satisfaction and loyalty.
Furthermore, AI text analysis can uncover emerging trends and changing preferences. By analyzing a large volume of reviews over time, businesses can identify shifts in customer preferences and adjust their strategies accordingly. This can give them a competitive advantage by staying ahead of the curve and meeting evolving customer demands.
Take the recent trend for "Shrinkflation" as a result of brands reducing pack sizes in the mind of the consumer or earlier this year, shoppers moaning about price rises. If you were Budweiser you would want to know there are some of your Top 3 visible reviews in ASDA:
Getting early alerts on the important trends that affect your brands and categories will put you one step ahead of the competition.
Driving category growth from consumer reviews insights
AI text analysis of product reviews can be a game-changer for brands when supporting supermarkets aiming to grow their categories. By delving into the textual content of reviews, AI text analysis can unearth previously unnoticed trends, preferences, and emerging demands within specific product categories. For instance, it can highlight a surge in consumer interest in organic produce, the increasing popularity of plant-based alternatives, or shifting dietary preferences.
The analysis can also highlight the most important category descriptions as used by shoppers. The words they use spontaneously are the same words they will use to search for products online. This analysis can therefore help with search and taxonomy. Individual comments can also bring alive fresh possibilities around product usage or NPD.
For the beer category in ASDA, below are the 25 most often used descriptions (vs other categories). These will be the same descriptions used in the search for example. To appear higher up search rankings one thing a brand could do is incorporate these into product descriptions or marketing messages.
Brands and retailers can drive category growth by using insights and data from AI text analysis to change hierarchies and product descriptions.
Talk to us at CheckoutSmart about how to get this information for your categories and retailers.
Leveraging shopper insights for success
The use of AI text analysis can be a powerful strategy for driving business growth. By analysing the sentiments and opinions expressed in reviews, businesses can identify areas for improvement and proactively address them. This can help enhance their products, services, or customer experiences, leading to higher customer satisfaction and brand loyalty.
In addition, AI text analysis can provide valuable insights for marketing strategies and campaigns. By understanding the language, sentiments, and preferences expressed in reviews, businesses can tailor their messaging and targeting to resonate with their intended audience. This can result in more effective marketing efforts, higher conversion rates, and increased customer engagement.
In summary, the use of AI text analysis can help businesses optimise their offerings, address pain points, and improve marketing strategies. By embracing this technology, businesses can gain a competitive edge and achieve long-term success.
To get your own category reviews analysis:
fans 15,785
followers 4,312