Data-Driven Decision Making: How Brands Utilize Consumer Insights
Gone are the days of relying purely on gut instinct to make head or tail of consumer insights. Today, most brands count on big data and consumer insights to make business decisions that exceed customer expectations. Indeed, 36% of marketers in a HubSpot survey said data helped them to reach their target audience more effectively. How do they achieve this? Through data-driven decision making, a process where brands collect consumer data, analyze it, and gain a valuable understanding of customer insights. This article unveils how brands are utilizing data-driven decision making to make sense of consumer insights.
A survey by McKinsey shows that 71% of consumers expect brands to deliver personalized experiences and interactions. There’s only one way to do it—collecting relevant data. To that end, brands are collecting data massively (at least 73% of all companies collect Americans’ personal data) and use data-driven decision making strategies to deliver bespoke experiences. Netflix famously analyzed customer insights derived from 3 million searches, 4 million ratings, and 30 million watch hours to drive the development of viral shows like Arrested Development and House of Cards. And the rewards are immense: providing personalized experiences may lift revenue by up to 15%, reduce customer acquisition costs by as much as 50%, and enhance marketing return on investment by up to 30%.
Speaking of personalized experiences, brands can also use the acquired consumer insights to send marketing content to a target audience. Analyzing data helps brands understand their audiences’ behaviors and preferences and build marketing demographics to enable laser-focused targeted promotions. Since Salesforce found that 57% of consumers are willing to share personal data for personalized offers, brands are utilizing customer insights to push targeted promotions. For instance, Red Roof Inn, which operates near airports, leveraged data-driven decision making to increase check-in by 10%. They utilized flight cancellation data and weather reports to optimize their marketing campaigns to target mobile users whenever there was a chance for flight cancellation due to bad weather.
Utilizing data-driven decision making principles is a key way to predict what could happen in the future, such as identifying emerging market trends or how many customers will stop conducting business with a brand (churn). By analyzing historical data, such as sales figures, the brand can identify critical issues like changing market conditions and the churn rate, which are vital in developing strategies to improve customer service and making other data-backed predictions. Amazon champions predictive analysis usage. The company relies heavily on analyzing buyers’ browsing and purchase history to make future product recommendations, enhance market fluctuation adaptability, and respond to unforeseen demand signals.
Collecting customer data will reveal consumer peak times, demand patterns, and resource use. Leveraging data-driven decision making aids brands in allocating resources like inventory and staff efficiently. Coca-Cola often relies on big data analytics, Artificial Intelligence (AI), and image recognition to maximize marketing efficiency. By identifying the locations of fans through photos they share online, Coca-Cola creates personalized and targeted ads. Additionally, sales data will unveil consumer product preferences. Brands can utilize this consumer insight to ensure they stock the right amount of inventory, minimizing the likelihood of insufficient or excess inventory.
At the heart of marketing is crafting the right message and reaching the appropriate target audience. Data-driven decision making is the most effective way to achieve this. Data analysis of a marketing campaign will shine a spotlight on the most effective marketing channels that drive the most traffic. Brands can use this knowledge to pinpoint where to ramp up or scale down their efforts.
Setting the price of a product may seem pretty straightforward, but companies get 30% of their pricing decisions wrong. Considering that a 1% increase in price translates to an 8.7% rise in operating profit, a lot is riding on getting the pricing decision right. Companies must juggle several products and numerous customer touchpoints to arrive at the right price, a particularly tricky affair. So, some brands rely on big data and data-driven decision making to determine the optimal price for each product. For example, a Wonderflow analysis of over 5 million online reviews found that brands offering discounts are more likely to receive positive reviews (30% on average) than negative ones (2%). On the other hand, supermarkets and hypermarkets, which generally maintain or hike prices, typically receive 14% positive and 10% negative reviews on average. Data-driven decision making has been a game changer in how brands run their businesses. Without big data, most companies would be making decisions blindly. Data-driven decision making has enabled brands to be more precise and efficient in their operations. For instance, brands are utilizing data-driven decision making to identify the most effective marketing strategies, allocate resources more efficiently, send targeted marketing content, and set optimal prices for all products.
