Data Science and Market Research

Market research is only one data source brands should consider when evaluating their brand. With 99.5% of data going un-tapped, we looked at a few different data sources that can inform pieces of your brand strategy.

Companies can achieve brand preference by creating tailored marketing campaigns, offering high-quality products, and excellent customer service. This can be accomplished through a combination of market research and data science, which allows companies to extract insights on customer loyalty, engagement, and brand awareness.

Customer Analytics

The advent of big data gives companies access to millions of data points on transactions, reviews, and social media histories of current and potential consumers. Many companies now employ data scientists to make sense of all this data and use methods such as regression, classification, and clustering to assess customer behavior and preferences.

This allows companies to answer question like these:

1. 1. What is the probability that a customer will buy a new product?
   2. What price and size combination yielded the highest sales last month?
   3. What types of words and phrases are associated with positive and negative reviews?

The answers to these questions help companies segment customers by product preferences, transaction patterns, and overall satisfaction. Business and marketing teams can in turn use this information to develop promotional materials or products that relate to the company’s brand image.

Maybe the brand image is quality products at an affordable price. Or it could be one that offers first rate customer service day or night. Regardless, companies that harvest the power of big data are often more likely to adapt to their customers wants and needs.

Audience Targeting and Customer Engagement

Companies are increasingly turning to the digital world to market their product and services. Therefore, it is important that they are targeting the right audience and engaging them, which can play a big part in boosting a brand’s online image.

On one hand, approaches like A/B testing can measure ad campaign performance on social media and Google ads. On the other, unsupervised learning, a technique that analyzes and creates clusters of data points, allows data scientists to explore patterns and similarities in user preferences for products and content that they might have missed using more traditional marketing analysis. Both methods can be combined to split segment users into groups based on response rates or qualified leads.

Simply put, those that explore and track audience preferences for online ad content across different demographic groups are better equipped to build a sustainable brand image. For example, digital savvy companies like Chewy, an online pet retailer, create video content ranging from health and wellness products to mobile ordering. This builds on its reputation as a trusted and convenient online platform for pet owners.

Market Research Also Builds Brand Preference

Market research is another tool used to create brand preference. This field combines qualitative and quantitative approaches to test the viability of a new (or existing) product or service.

Specifically, market research uses surveys, product testing, and focus groups to gain insights and real-time opinions from target demographic groups. These techniques can aid companies in understanding questions like ‘what new candy flavor and color to move into production’ or more general ones like tracking ad response rates in different regions or improving customer experience for online orders.

Like data science, market research can uncover preferences and pinpoint the customer’s perceptions and emotional responses surrounding the brand.

Wrap Up

Almost any successful company has a brand image. Data science and market research are two methods that can help companies build brand awareness and differentiate themselves from competitors. Those that succeed are more likely to stand the test of time.