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First up, consumer insight teams may use text mining to understand their customers’ pain points and to understand why customers choose to use their product/service. Which in the end is the unique selling proposition.
Let me make a strange, but clear, example: a company that sells vacuum insulated lunch boxes, for example, might know that their product caters to several categories of consumers — including those who would like to eat healthier, those who are trying to save money and more.
Now, say the company analyzes their data and finds out that the vast majority of their customers are dealing with a weight-loss issue. Now that the company knows that their consumers are choosing to pack their lunches so that they can have better control over the food they eat, they can use this information to fine-tune their messaging and pitch accordingly.
Website copy and messaging aside, if the company is looking into expanding its content marketing strategy, they might also create more marketing collaterals that teach consumers how to prepare healthy meals and use their lunch boxes to maintain a healthy lifestyle.
Text mining is also valuable for companies who are trying to look into whether their product or service lives up to consumers’ expectations.
It’s fairly simple to pick out positive or negative sentiment here — if your product reviews contain a lot of positive language such as “fantastic”, “great”, “useful”, then you’ll know your on the right track. If your product reviews contain negative phrases, on the other hand, then it’s worth taking a closer look at why your customers are unsatisfied and improving upon your product accordingly.
Moving on, text mining can also help you benchmark your performance against your competitors’.
In product reviews, consumers tend to compare the product at hand with similar products they’ve used. Here, they might point out how your product is advantageous in certain ways, but doesn’t perform well in other ways.
With this information, consumer insight teams get a better idea of how their brand stacks up against the other brands in the market and decide if they need to respond by improving their product. If the brand that the team is managing is priced at a premium and the company justifies this price by asserting that their product is the most effective one in the market, then they’ll definitely want to look at tweaking the product to live up to those claims.
For more use cases, see how Wonderflow is being used by the world’s most customer-centric brands on our Use Case page.
Running in the same vein, text mining also allows you to uncover what your consumers like and dislike about your product.
You’ll likely find some conflicting information here — Consumer A might state that they like Feature A, but Consumer B might assert that Feature A is useless and doesn’t do much for them. Bearing this in mind, look at what the data says as a whole instead of getting swayed by outliers and anomalies.
If you have more data (such as data on your customers’ Lifetime Value) and you have it by hand, think about analyzing your text in conjunction with this additional data. You might find that:
- The majority of consumers who don’t like Feature A tend to have a low lifetime value and won’t stick around long, and that
- Your customers who make repeated purchases have no issue with Feature A.
If that’s the case, there’s obviously no need to tweak your product to satisfy your once-off customers.
You can discover surprising insights about how your customers use your product via text mining and text analysis.
For example, if you sell a travel kettle and you find that several of your customers are talking about how they used their kettle to cook pasta while they were on the road, then you’ve just discovered a whole new use case for your product. Depending on your overall strategy, you might decide to play up this use case and showcase it as an additional product feature.
Consumer insight teams can also rely on text mining to flag out safety issues and other concerns that are important for the company to note.
For instance, if teams conduct a text analysis and find that many customers are talking about how their product was dangerous to use or malfunctioned in some way, then it’d be a wise move for the company to do some investigating and find out if there were problems that arose in manufacturing.
If that’s the case, the company might then do a product recall and compensate their customers. This might not be an ideal situation, but it’s preferable to dealing with scathing, reputation-tarnishing reviews on social media, or worse, having a customer take you to court.
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Analyzing multi-language customer feedback in large volume from different sources is a complex process. With an AI-based technology and years of experience, Wonderflow is helping global brands to become customer-centric. Find out more about our solution.
Wonderflow empowers businesses with quick and impactful decision-making because it helps automate and deliver in-depth consumer and competitor insights. All within one place, results are simplified for professionals across any high-UGC organization, and department to access, understand, and share easily. Compared to hiring more analysts, Wonderflow’s AI eliminates the need for human-led setup and analysis, resulting in thousands of structured and unstructured reviews analyzed within a matter of weeks and with up to 50% or more accurate data. The system sources relevant private and public consumer feedback from over 200 channels, including emails, forums, call center logs, chat rooms, social media, and e-commerce. What’s most unique is that its AI is the first ever to help recommend personalized business actions and predict the impact of those actions on key outcomes. Wonderflow is leveraged by high-grade customers like Philips, DHL, Beko, Lavazza, Colgate-Palmolive, GSK, Delonghi, and more.
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