The current landscape of online cosmetic reviews has it all—mountainous highs, oceanic lows and a moderate middle ground. Even the most popular, influencer-hyped blush-and-contour palette will receive both five-star and one-star reviews on retail sites like Sephora and Ulta, review aggregators like Makeup Alley and—if brands are being transparent—on their own sites too. It all leaves customers wondering who to trust and brands wondering how one person can unequivocally love a product while the next wants to throw it away?
Ironically it’s that wide variance of customer feedback that can ultimately answer that question, as well as deeper and more complex questions about customer behaviour (including around why customers write reviews in the first place and how accurately their opinions reflect the products themselves).
Data insights hold the key to crafting a CX strategy that is authentically customer-focused and has an impact on multiple departments within a company, from product development to customer service.
AI data analytic solutions lets brands discover the value within thousands of unique reviews and uncover the complex human motivations behind customer engagement and behaviour. Those data insights hold the key to crafting a CX strategy that is authentically customer-focused and has an impact on multiple departments within a company, from product development to customer service.
Data analysis marks the competitive edge of CX
Beauty is a competitive and ever-crowded business, currently worth $532 billion and expected to advance by 5%-to-7% a year by 2025. Legacy brands now compete with emerging brands, all-natural cosmetics startups, and direct-to-consumer brands that stress personalized products and follow-up services. That abundance of choice has spurred innovation in products and services, marketing strategies, retail opportunities and customer service across multiple channels. It’s also given customers more avenues to voice their opinions and listen to the opinions of others. But are brands listening and responding in the best ways? This is where data-informed CX makes a measurable difference.
Due to beauty-focused online video content, an increase in natural beauty products and a surge in direct-to-consumer and exclusive brands, skincare is one of 12 industries that millennials are boosting. The common thread behind this skincare industry boost is peer communication through word-of-mouth and written reviews. Inherent in that is trust between reviewers, as well as trust between reviewers and brands.
With the vast majority of people regularly or occasionally reading online reviews today, customer feedback data can:
- Reveal a path-to-purchase
- Add dots to the customer journey map
- Impart not only opinions about a product but how much customers trust a brand
- Determine where customer loyalty is gained or lost
It all adds up to getting to know your customers better and offering a customer experience that lives up to the expectations they’ve expressed.
The increase in customers’ online engagement, purchasing and peer-to-peer communication has spurred a total rethink of contemporary CX strategy, especially in regard to data use.
The increase in customers’ online engagement, purchasing and peer-to-peer communication has spurred a total rethink of contemporary CX strategy, especially in regard to data use. Deloitte’s recent CX marks the spot consumer review reports,“Businesses need to use advanced tools to collect the right data about individual consumers and use it to respond in real time to their specific needs.” CX has become increasingly personalized, with strategists intent on mapping what consumers want and expect throughout their brand journey.
Increase personalized CX through data
With every unique customer comes unique needs. By that logic, one product simply can’t suit every customer and won’t be able to meet everyone’s standards. In the past, customers lived with that fact and moved on to purchase other products, hoping they would work for them. Today, with the help of AI-driven data, personalization in CX is beginning to translate into personalization of products and services.
Cosmetics startup Proven Beauty uses AI tools in analysis of customer surveys and reviews to personalize their products to suit each person’s skin needs. The company works on the premise that customers are tired of products that don’t live up to promises—they want both a product that works for them and a brand that upholds its promises. Marrying CX with customized products, Proven Beauty is gaining loyal customers and opening customers’ eyes to personalization’s possibilities.
Brands can reach similar goals when they apply AI analysis tools to customer reviews of their products, lending a valuable and data-informed CX perspective to any changes in products and services. Brands that create this kind of broader personalized ecosystem through data will help empower customers and gain their trust.
Turn customer reviews into actionable insights
AI data analysis lets CX leaders discover the deeper context of customer reviews, including how customers’ past experiences, cultural and physical environments, psychology and other personal factors affect how they feel about a product or service and what motivates them to purchase it. In that way, brands can find value in promoters, detractors and even fake reviews.
Brands that create this kind of broader, personalized ecosystem through data will help empower customers and gain their trust.
For example, Keatext’s AI analysis technology is able to differentiate between reviews that focus on products themselves and personal reactions. A complex review such as, “I loved this product because it felt so good on my skin, but it made me break out after two weeks” is analyzed to show both its positive (“I loved”) and negative (“break out”) connotations alongside the reasoning behind them, and identify that it is a personal assessment (“me”) related to an individual’s reaction. Meanwhile, another review might read, “This product smells like roses” and receive a one-star review. Data analysis can determine that while “smells like roses” might be negative for the reviewer, it is also simply a statement of fact (and likely positive for other reviewers!)
Using sentiment analysis on unstructured data, Keatext’s AI technology is able to show multiple, overlapping classifications within customer’s reviews, determining between subjective and objective wording, opinion and fact, negative or positive, comparative or direct, and so on. The insights gained through this deep level of analysis can help uncover the complex human motivation behind customer engagement with a brand.
Insights gathered from reviews leads to engaging and retaining customers through more personalized CX strategies that include the ability to address customer concerns and recommendations that thread throughout the customer journey. Customers feel listened to and their loyalty increases when brands respond and take relevant action based on their feedback.
In a broader business context, where CX leaders need to prove ROI to executives, customer reviews represent a form of small data found within the customer journey. When analyzed and used in combination with big data’s broader metrics and insights into business outcomes and trends, the small data from reviews, CX surveys and other feedback makes a measurable impact on the success of CX strategies and their positive link to a company’s bottom line.
