Opportunities and strategies for gaining data-based insights
For many leading companies, the effective use of big data analytics is the decisive factor in operating successfully and staying ahead of the competition, even in the face of volatile market developments. In many industries, more and more new entrants and established competitors are therefore using data-driven strategies.
For example, to record and analyze their performance and anticipate developments. There are also use cases for big data analytics in almost all sectors, from technology companies to the healthcare sector. Marketing departments in particular benefit greatly from the use of these enormous amounts of data. But what exactly are these benefits? First of all, it must be clear when we can talk about big data at all.
What is big data?
Defining big data in concrete terms is still a challenge in the industry. Gartner defines the term on the basis of three key factors: the size of the data (volume), the speed with which the respective data is generated, evaluated and processed (velocity) and the variety of data types and sources (variety).
Over time, two further parameters have been derived from these three factors. These are the credibility of the data (veracity or validity) and the added value that results from the data for a company and its decision-makers. This value is defined by the four preceding factors.
The key to this added value is the acquisition of new information. This enables companies to gain improved insights into business processes and to initiate decision-making and process automation.
When generating, evaluating and processing these enormous amounts of data, companies use combinations of processes from different disciplines. Knowledge and methods from computer science, statistics and mathematics are used in data analytics and data science as well as machine learning and deep learning in the field of artificial intelligence.
The use of big data in marketing
Collecting large amounts of data does not automatically lead to better marketing. Rather, it is new findings from large amounts of data, the decisions and the measures taken as a result that make the difference.
These new insights can provide marketing staff with insights into what content is most effective at each stage of a sales cycle. Consequently, how investments in customer relationship management (CRM) systems can be improved. The valid measurement of KPIs that are important for marketing can optimize strategies to increase conversion rates, prospect retention, sales and customer journeys. Furthermore, by analyzing large amounts of data, companies can make statements about important key 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 should take into account specific insights, such as the cost of the next best competitor product compared to the value of the product for the customer. Based on such insights, they can calculate their own optimal price. For a company with only a few products, this type of pricing is of course easier than for a company with a wide range of products. It is simply too time-consuming and a significant cost factor for large companies to manage the complexity of these constantly changing price variables for hundreds of products
Algorithms based on big data now automatically calculate the optimum price for a product. Automated systems can identify similar products. They then determine what constitutes the value of this product. This is then compared with historical transaction data. In this way, companies can set prices for product groups and segments based on data. Automation makes it much easier to adapt and control these analyses to developments on an ongoing and variable basis.
Customer value analytics and customer relationships
Customer value analytics (CVA or customer value analysis) can be used to identify profitable customers. This makes it possible to determine whether it is profitable to maintain a business relationship with a customer. Secondly, whether it is worth investing in them in the future. Customer value analyses play a particularly important role in mail order and online retail, in the optimization of disposition behavior and in the returns management of an online retailer.
CVA based on big data enables marketers to deliver consistent customer experiences across all channels. CVA is evolving into a sustainable set of practices based on big data. They accelerate sales cycles and scale while maintaining the personalized nature of customer relationships.
By using big data analytics in combination with CRM systems to define and manage customer development, marketing professionals increase the potential to create stronger customer loyalty and improve customer retention.
Personalized offers
The creation of personalized offers with the help of big data analytics is another example. Based on the findings from a user's customer journey, personalized customer treatments are designed for specific follow-up actions. For example, the prevention of customer churn or the invitation to make a subsequent purchase.
With location-based data, companies can present contextual offers at or near a store or shopping center. Location analytics also has broad applications for improving the customer experience and will become a must-have for designing, managing and measuring customer experiences.
Improvement of SEO, email and mobile marketing
Digital marketing via email, messenger, search engines and social networks are the basis of successful multi-channel marketing. The volume and immediacy of data generated from these marketing channels can provide insights that help marketers customize audiences, create tailored offers and marketing content, and make quick adjustments to marketing campaigns. When a company takes active steps to provide and implement big data for SEO strategies, it can have a big impact on a website's traffic. For example, by evaluating A/B testing in large volumes to continuously adjust ads and banner ads.
Reducing costs with big data analytics
Consequently, understanding customer profiles is of enormous importance for companies. A few years ago, the process of investigating customer behavior was still carried out manually in order to develop suitable marketing strategies. With digitalization and the amount of information generated, these processes have also become automated. They are a real treasure trove for companies, as customer profiles are made up of data that goes far beyond the actual purchasing or ordering process. Demographic data, connection data and meta-information about customer behavior before and after the purchase can be included in the evaluation.
Nowadays, marketers can develop the analysis of this data into profitable information about the composition of their customers in order to evaluate customer behavior and make strategic business decisions. To this end, processes can be adapted and insights gained from buyer and user behavior that avoid 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 is particularly important for large amounts of data. Without a specific project and the formulation of a use case, data does not become information and remains pure numbers. The correlations must be established by marketers themselves and the data must be examined for concise questions.
Fortunately, the volumes of data generated in marketing form a broad field that enables sufficient analysis for a wide variety of KPI developments. Marketers should therefore focus on big data analytics with a clear vision in order to analyze relevant developments and gain pointed insights.
