Why use big data to mine potential users?

With the development of the Internet and the intensification of competition in the consumer market: new brands, new tracks, new channels, and new marketing methods are emerging. Under the rapidly evolving market landscape, how to build competitive barriers and sustain growth requires a renewed foothold in the context of the rise of new consumption in the digital era, insights into the consumer experience as the core, reshaping brand values, and a meticulous layout of growth strategies.

Only a comprehensive and detailed mining of consumers' mind changes, such as consumers' age, gender, consumption habits, life status, points of interest and other information, can provide the right direction for the next internal innovation. High-quality consumer experience is the key to enhance brand loyalty, but also an important foundation for enterprises to maintain a stable profit model. With the development of the Internet and the intensification of competition in the consumer market, every social media post, every social interaction and every online purchase reflects the consumer habits, attitudes and behaviors. Collecting and analyzing this data and making effective decisions about the consumer experience is a critical part of a company's business, and a differentiator for growth.

How do you determine your target consumer?

Traditional market research - time-consuming, labor-intensive, costly, limited sample size, and the possibility of respondents hiding their true thoughts.

Social media big data - in line with the user's communication and online behavior habits, without manpower, data can be automatically collected around the clock, the amount of data and analysis dimensions are richer, more objective, more credible .

Traditional user data collection has the following challenges:

01 Online and offline customer experience touchpoints, fragmented information dispersed in the various departments of the enterprise, can not use the integrated data to quickly understand the consumer demand and customer experience, to empower management decision-making.

02 Traditional research has a small sample size, a long implementation cycle, and the statistical results often lag behind consumer trends, making it difficult to transform them into actionable insights to empower product innovation and marketing growth.

03?Market intelligence data sources are thin, making it difficult to cope with the rapidly evolving competitive landscape of the market, and the lack of a unified tool for competitive benchmarking makes it impossible to know your enemy and know yourself.

So, how to comprehensively understand the target population, label and analyze

consumer experience insights based on real-time big data and machine learning algorithms is an effective solution for organizing the allocation of enterprise resources in a truly "consumer-centric" way. Consumer experience insights can help companies quickly collect and understand consumer demand, product reputation, competitor dynamics, new product trends, and consumer hotspots, and then drive professionals in marketing, R&D, customer experience, retail operations, and other functions to seize business opportunities and respond to the fast-changing consumer market in an agile manner.

The first step is to segment the crowd -- understand who they are, where they are, and what they like.

Best Practice Example (Food & Beverage)

A well-known international restaurant chain brand wanted to understand the core consumer groups and segmentation of the Chinese coffee market. After applying machine learning modeling, over 1.2 million consumer reviews and social media, e-commerce, and short-video discussions related to the brand and competitors were clustered and analyzed to identify four core consumer segments.

The DataTouch data analytics platform, combined with industry category distribution data, allowed analysts to further analyze the drinking environment, tastes, and packaging of different pain points of the segmented populations, and combined with the brand's strengths, weaknesses, and characteristics of the populations to give targeted recommendations for the brand's future precise product positioning and communication strategy to provide a strong basis for decision-making.

The second step is to guide the product communication strategy based on the segmentation of the crowd, capturing opportunities for segmentation and differentiated product concepts

After understanding the market landscape and product innovation direction, the client wants to understand the segmentation of the target trend category in the core innovation direction. Using machine learning modeling, the client conducted a cluster analysis of nearly 10 million consumer reviews and social media, e-commerce, and short-video discussions related to each innovation direction, and sorted out 4-5 core consumer segments.

The DataTouch data analysis platform then combines industry category distribution data, brand competition patterns and customer experience satisfaction, with analysts further analyzing the lifestyle, scenario needs, market share, opportunity positioning of the segmented population track, and NLP deep learning sentiment analysis of each product attribute (efficacy, usage experience, product form, packaging, etc.) to refine the unmet pain points and appeals. Refinement of unsatisfied pain points, combined with brand positioning strengths and weaknesses and crowd characteristics to give innovative product differentiation recommendations for the brand's future precise product positioning and communication strategy provides a powerful data insight-driven basis for decision-making.