Collecting large amounts of data does not automatically lead to better marketing. Instead, it is new insights from large amounts of data, the decisions, and the measures taken as a result that make the difference.
These new insights can provide marketers with insights into what content is most effective at each sales cycle stage. Consequently, customer relationship management systems (CRM) investments can be improved.
Valid measurement of the KPIs important for marketing can optimize strategies to increase conversion rates, prospect retention, sales, and the customer journey.
Furthermore, by analyzing large amounts of data, companies can make statements about vital critical figures such as customer acquisition costs (CAC), customer lifetime value (CLV), and many other customer-oriented factors.
Improving The Pricing Strategy
For each product, companies should be able to find the optimal price that customers are willing to pay. Ideally, they consider specific insights, such as the cost of the following best competing product versus the product’s value to the customer. With such insights, they can calculate their optimal price. Of course, this type of pricing is easier for a company with only a few products than for a company with a wide range of products.
Managing the complexity of these ever-changing price variables for hundreds of products is too burdensome and a significant cost factor for large companies.
Algorithms based on big data now automatically calculate the optimal price for a product. Automated systems can identify similar products. They then determine what constitutes the value of this product.
As a result, this is matched against historical transaction data. It allows companies to price product groups and segments based on data. Automation makes it much easier to adapt and control these analyzes continuously and variably to developments.
Customer Value Analytics And Customer Relationships
Customer value analytics (CVA or customer value analysis) can be used to identify profitable customers. It makes it possible to determine whether it is beneficial to maintain a business relationship with a customer. Secondly, whether it is worth investing in them in the future.
Customer value analyses play a significant role in optimizing planning behavior and the returns management of an online retailer, particularly in mail order and online retail.
CVA powered by big data enables marketers to deliver consistent customer experiences across all channels. CVA is emerging as a viable set of techniques based on big data. They accelerate sales cycles and scale while maintaining the personalized nature of customer relationships.
By combining big data analytics with CRM systems to define and control customer development, marketers increase the potential to create stronger customer relationships and improve customer retention.
The creation of personalized offers using Big Data Analytics is another example. Based on user journeys’ insights, personalized customer treatments are designed for specific follow-up actions. For instance, preventing customer churn or prompting you to make a subsequent purchase.
With location-based data, businesses can present contextual offers at or near a store or mall. Location analytics also has broad applications to improve customer experience and will become a must for designing, managing, and measuring customer experiences.
Improving SEO, Email And Mobile Marketing
Digital marketing via e-mail, messenger, search engines, and social networks is the basis of successful multi-channel marketing. The volume and immediacy of data generated from these marketing channels can provide insights that help marketers tailor audiences, create tailored offers and marketing content, and make quick adjustments to marketing campaigns.
When a company takes active steps to deploy and implement big data for SEO strategies, it can significantly impact a website’s traffic. For example, evaluating A/B testing on large scale results in continuous adjustments to ads and banner advertising.
Reducing Costs With Big Data Analytics
Consequently, the understanding of customer profiles is of enormous importance for companies. A few years ago, the process of examining customer behavior was still carried out manually to develop suitable marketing strategies then. With digitization and the amount of information generated, these processes have also become automated.
They hold a real treasure for companies: Customer profiles are made up of data that goes far beyond the purchase or ordering process. Demographic data, connection data, and meta information about customer behavior before and after the purchase can be included in the evaluation.
Today, marketers can further develop the analysis of this data into valuable information about the composition of their customers to evaluate customer behavior and make strategic business decisions. In addition, processes can be adapted, and knowledge gained from buyer and user behavior avoids wastage and, thus, unnecessary costs.
Big Data Analytics: Lots Of Potential For The Right Questions
As with all data analytics initiatives, the type of question and the need for knowledge with which the data sets are to be questioned are significant for large amounts of data.
Without a concrete project and the formulation of a use case, data does not become information and remains pure numbers. Marketers must create connections and examine the data for concise questions.
Fortunately, the amounts of data generated in marketing form a broad field that enables enough analysis for the most diverse KPI developments.
Marketers should therefore turn to data analytics in a focused manner and with clear ideas to analyze relevant results with specific gains in knowledge.