Advanced Analytics is the further development of BI (Business Intelligence). Predictive analytics as a relevant area of advanced analytics focuses on predicting future events and their probabilities using techniques such as data mining or neural networks in order to solve complex business problems and obtain a 360-degree view of the company and the customer.
An important field of application of machine learning in marketing is personalisation. Machine learning can learn customers' preferences and behaviours and compare them with those of other customers - the result is, for example, individual product or action recommendations for each customer at the given time.
All measures and analyses are based on a process that considers all relevant data from start to finish and makes it usable. This ensures a comprehensive analysis that includes the points in time and relevant customer interactions.
Based on all relevant data points, the Data Platform offers a comprehensive system of tools. Starting with data preparation via analytical mechanisms from the area of marketing automation to channel control and the implementation of marketing campaigns including comprehensive reporting.
Integrated process of combining all distributed marketing data, standardising it for the purpose of comprehensive data analysis and evaluating it on the basis of predefined parameters. In this way, the success of marketing activities can be calculated holistically and the achievement of growth targets by channel, target market or customer segment become measurable. In addition to analysis, channels can also be actively controlled on the basis of their target KPIs.
Comprehensive data and tool-supported method to automate marketing processes efficiently and sustainably. Combined functionalities from CRM systems, web analysis, email marketing, social media advertising and retargeting to generate and qualify qualitative leads and convert them efficiently (e.g. into sales).
Data-based identification and playout of individual triggers (e.g. price, content, offer, etc.) that lead to the defined Call-2-Action, e.g. lead generation via information request, purchase conclusion, price acceptance, etc.
Analytical marketing approach that identifies the best next offer for each individual customer. This customised marketing approach increases cross-selling and up-selling in the existing customer base and enables companies to respond individually to customer needs, which in the long run leads to stronger customer loyalty and an increase in customer lifetime value.
For each customer, individual relevance means something different. Based on their usage behaviour but also their personal circumstances such as needs and motives, we use a wide range of parameters in the automation of marketing campaigns, which makes hyper-personalisation at the customer level possible with little manual effort at the same time.
Old email workflows to customers can be a big area to revitalise. With lengthy processes and siloed data, businesses lose out when it comes to productivity and leveraging important customer information. By using a communication engine, you can leverage targeted data. By setting up action triggers with your communication/marketing systems, the system knows when to engage with customers, which message best fits the individual situation and which channel should perform the interaction. With these actionable insights, you can revolutionise your customer experience and tailor it to their specific needs.
Use data to accelerate your business strategy: A business strategy is critical for any company that wants to strategically grow its business. A data-driven business strategy ideally combines the best practices of data science with the best practices of the business model for e.g. higher efficiency, better performance, more productivity, higher profits and lower costs.
Data-driven branding is an efficient tool in the age of e-commerce and social storytelling: a brand story is the heart of a brand. It should be relevant and credible - and confirmed as such with ample evidence. Gathering data from, for example, forums external to the brand can show how the brand story is resonating in the real world. Data can show how the brand story is developed, retold and shared in consumers' eyes. This feedback can be used to validate a brand's current direction, but it can also help a brand team refine a data-driven brand story for greater relevance and impact.
Product teams face a never-ending battle to develop new features, offerings and innovative products for customers. Product development today benefits significantly from data-based insights. Data from the use of existing products provides a basis for adapting features or in redesigning new products. Sensors in devices already in use show which functions are used preferentially and the evaluation of social media data can reveal new trends and the wishes of customers.
Data governance is the overall management of data availability, relevance, usability, integrity and security in a company. This enables the company to manage its information-related knowledge and use it in a targeted and legally compliant manner, e.g. for data analytics topics. Digital and high-quality data are the basic prerequisite for companies to meet customer requirements: With the help of data governance, the foundations for improving and securing data quality in companies are established - which means that data can be used more efficiently in the medium term.
The prevalence of data silos and the presence of fragmented devices show that it is still a major challenge for many companies to identify individual customers at a granular level and target them with personalised content and experiences - this is where the use of a holistic Data Driven CRM - system is recommended.
Providing the right customer service should be a priority for every business, regardless of size. Companies that deliver this exceptional customer service are data-driven, using data from a variety of different sources. Being data-driven allows your customer service team to report compelling insights based on accurate information. These insights are better suited to understand next steps to resolve customer issues and provide better support in the future. In addition to providing a better customer experience, our data-driven approaches offer the opportunity to optimise costs in service through automation or to transform from cost centres to profit centres through innovative cross-selling and upselling approaches.
Data-driven sales is a sales approach where sales teams collect data and use it as the basis for every decision - from the products they sell to the time of day they engage prospects and customers. Implementing a data-driven sales approach can make companies significantly more profitable. The basis here is a prediciton model that targets the probability of closing deals by taking into account existing sales insights.
A schema tailored to your business and your customer base, with clear derivations of which business levers can be taken in the different customer or product segments. The Value Matrix provides a structured and analytics-based foundation for planning impacts into prioritised use cases, enabling management decisions to be more informed and successful.