Digital channels are crucial for a positive customer experience on the one hand and for the business success of companies on the other. The following measures are available for redesigning and optimizing the customer experience in order to precisely meet the needs of customers and provide them with the best possible service and shopping experience:

1. Compulsion to digitize due to changing customer behavior:

The pandemic has seriously changed the world of life. Stationary retail has shifted toward online retail. As a result, digitized offerings have become essential for businesses so that they can align with their customers. To create an on-demand and intuitive customer experience across all channels, this „new normal“ has significantly accelerated customer-centric business transformation. Building digital channels is not only convenient for every customer, but also necessary.

2. Personalization as a requirement:

With the expanded customer base, companies are required to optimize their customer experience strategy to generate relevant value for both customers and the company itself. A seamless and personalized customer experience along the entire customer journey is therefore of high importance. A customer must be offered products and services that he needs. This process is dynamic, depending on constantly changing parameters. For this, comprehensive analysis of data is indispensable.

3. Data-based and automated interactions:

Leading companies across industries collect and analyze data to gain relevant insights into customer behavior across all touchpoints. Once the company has developed a complete picture of customer behavior, the data can be used to target individual needs. This is where automation comes into play. Automation can increase targeted efficiency and streamline operations. The use of artificial intelligence is also being increased to identify customer concerns, especially in early interaction phases, to answer them and to trigger follow-up processes.

4. Modern technologies ensure a positive customer experience:

For business success, optimizing the customer journey through the intelligent use of data and automation is crucial to delivering a positive customer experience. This requires implementing a digital-first strategy that includes secure cloud-optimized solutions, artificial intelligence, automation, open APIs, analytics, and data management.

The design of the customer experience will be an important topic for companies in the coming months. The companies that have an eye on optimizing their customer experience and customer journey and do everything in their power to improve customer interactions will be well on their way in the New Normal. Because there will be no turning back in the future from the „transitional normal“ of the lockdown periods and the customer experiences learned during this time with many new digital approaches.

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A common misconception is that machine learning, business forecasting, business intelligence and anything related to predictive analytics is synonymous. For this reason, the following article focuses on taking a closer look at the definitions and applying them to the context of predictive analytics.

Business Forecasting is the term used to describe the process of extracting information and gaining insights. Machine Learning, on the other hand, is the application of Artificial Intelligence and is more of an approach than a process. Business Intelligence, on the other hand, describes the different types of analyses and results.

Business Intelligence
Business Intelligence, or BI, is a technology-based process for analyzing data and presenting actionable information that can help organizations make informed business decisions. Insights from business intelligence tools as suggested by Jimmy John Shark can be used to make strategic business decisions that improve productivity, increase revenue and accelerate growth. For example, valuable competitive advantages over the competition can be gained in this way. More importantly, it also reveals business problems that need to be addressed in the future.

Predictive analytics in business intelligence has become a necessary advancement in decision-making capabilities and insights. Most business intelligence focuses on descriptive analysis and visualization of data. Predictive analytics starts at this point, questioning what might happen in the future, and beyond that, even what you might do as a business. Predictive analytics is able to present information to help executives, managers, and others involved in the organization make business decisions.

Business Forecasting
Business Forecasting or rather business forecasting refers to the process of using analytics, data, insights to make predictions and answer business needs. While business intelligence is about tools and presentation, business forecasting is about analysis and process.

Predictive analytics in business forecasting has evolved into an advanced process that includes a variety of different data types, predictive causal models, more advanced algorithms and technologies. Various tools, data mining methods, forecasting methods, and analytical models are used to analyze historical and current data, assess business risks and opportunities, and make predictions. Predictive analytics involves not only internal sales history, but rather external variables, different influencing factors, and other data to improve predictions.

Machine Learning
Machine Learning is a subset of Artificial Intelligence and a collection of different techniques and methods that allow systems to learn automatically. These systems can be „trained“ to learn patterns from inputs that subsequently recalibrate from experience without being explicitly programmed. Unlike other approaches, these techniques strive to learn on their own as new data is presented and can make predictions and evaluations on their own.

Predictive analytics in machine learning is a category of approaches that achieves better predictions, improved intelligence, automation of processes, and the path to artificial intelligence.

Located at the intersection of advanced business forecasting, sophisticated business intelligence and machine learning techniques, predictive analytics leverages advanced business and planning processes to provide more information with improved efficiency. It’s not just about advanced analytics outputs and business intelligence, but also about helping companies gain insights into why and what things will happen in the future.

With many years of experience spanning more than two decades, Dastani Consulting is considered a predictive analytics pioneer. The team of Dastani Consulting GmbH looks back on many years of experience and thus enables the implementation of individually tailored solutions for each customer. The intelligent predictive analytics forecasts are able to anticipate the entire behavioral pattern of customers within a defined time horizon in order to derive optimal business recommendations – especially for marketing and sales.

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Crisis boosts use of AI
It is clear that the Corona crisis will continue to promote the use of AI. Classic sales situations – as seen in face-to-face visits between customer and salesperson – will still not be possible in 2021. This in turn means that digital sales will not only temporarily replace the classic situation, but will become increasingly established and completely change the way companies sell their products and services to each other. If companies rely on AI-supported sales, relevant speed advantages and ultimately even new sources of revenue will result. But companies must first learn how to use AI, and that takes a lot of time and experience to anchor the new technology into established work structures.

Digital Selling
Increasingly, B2B-sales are expected to support customers in their complex buying decision processes. At this point, AI delivers added value by providing the right products or service, the optimal price and suitable configuration options. Historical transactions and machine-learned experiences about the respective customer serve as the basis for this. Since AI can perform real-time analyses, it is able to evaluate conversations and interactions.

Due to the high number of sales channels and product options, sales teams are barely capable of performing rational evaluations. Therefore, AI is crucial to ensure the best customer buying experience and optimize the customer lifecycle. However, it is important that the technology delivers a high level of transparency in the decision-making process.

AI needs humans
While AI helps humans, it also needs human support. AI relies on data provided by humans and requires feedback on whether the data is being used wisely. Especially in current times of Corona, where the data foundation is not available, human influence on AI is therefore indispensable. Only through an expert assessment can meaningful courses of action be designed from the AI analysis. Humans and their intuition as well as qualified employees will not be made redundant by AI, but in pandemic times, judgment will be more important than ever for the year 2021.

Data will become more valuable
Moreover, data will become even more valuable in 2021. If target groups are unknown to companies and there is not enough information on, for example, key contacts or end consumers, attractive marketing activities cannot be launched. A good data basis in turn means a good AI solution.

Development of a digital strategy
Furthermore, a digital strategy will develop from the business strategy. AI solutions help to forecast the needs of the respective customers with pinpoint accuracy. This allows companies to think about launching new products and brands at ideal times, analyze the optimal price for them, and align the findings with their business strategy.

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After the extremely turbulent year 2020, the question for many B2B companies is how marketing and sales can work together in the future to regain their (old) strength? On this occasion, the special features of B2B companies and their consequences in terms of marketing and sales were the main topics of the day.

With the help of numerous use cases, which are described in this article in broad outlines, our CEO demonstrated at the intensive seminar at the University of St. Gallen how predictive analytics methods can be successfully used in marketing and sales.

Most projects fail at the interface to marketing and sales practice. A challenge is therefore the acceptance of predictive analytics in the company and the integration as well as training measures of all actors, the integration into systems with a transparent and user-friendly design and the acceptance of the learning curve – because every new technology requires a certain amount of time and experience.

New customer acquisition
In the first use case, the application of Target Group Predict was presented on the basis of a company in the automotive industry. The problematic initial situation was that there was no structured acquisition of new customers and leasing was not actively promoted. Using the affinity prediction model, suitable target groups were determined and, by using a specially tailored lead system, high-quality leads for the retail sector were identified. This in turn was reflected in a high level of satisfaction among sales staff.

Exploitation of potential
In another use case of a company in the tools trade, the acquisition of new customers did not take place systematically because the sales department focused on the high-revenue and active customers. The problem here was that the actual market potential of customers was not known and the potential could not be exploited – in a mass business with intense competition.

On top of this, the share of wallet analysis was also applied, by forecasting the sales potential of the company’s inactive existing customer base. The continued application of Next Best Offer and Customer Value Prediction also made it possible to predict which products might be of interest to which customers for their next purchase. With the additional use of the Sales and AIMS app developed by Dastani Consulting, the sales force was able to access the new and reactivation addresses in the area at any time and use Next Best Offer recommendations to approach customers. As a result, the valuable sales time could be used more effectively because potential customers in the vicinity were sorted according to their sales potential in the app. As a result, sales with the inactive customer base increased and now account for approximately 5% of total sales.

Effective market cultivation
Using another example from the intralogistics industry, it was made clear that the company under consideration was not optimally positioned for the following challenges due to the emerging cut-throat competition. Predictive analytics forecasts served as input for lead generation here as well. Sales and purchase expectations were forecast for individual products and product groups (Customer Value Prediction) and affine addresses for telemarketing were identified (Target Group Predict). As a result, the lead conversion rate developed to 16% and 15 million Euro more sales could be generated from new customer acquisition alone.

Opportunities in sales and marketing
Dr. Parsis Dastani showed in his exciting presentation at the intensive seminar how Artificial Intelligence (AI) is able to optimize distribution costs. The application of predictive analytics methods brings with it new opportunities for sales and marketing, which should be used now at the latest – after all the turbulence in 2020 – in order to survive in B2B competition and gain in (old) strength.

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The latest features that are on the rise in the field of information management are defined as Predictive Analytics or AI (Artificial Intelligence/Machine Learning). The era of AI is just beginning in many companies with the use of the many possibilities of Predictive Analytics, which are listed in this article for some cases. Furthermore you will read what you need to know about AI and Predictive Analytics and how they differ.

Definition: Predictive Analytics
Predictive analytics uses historical data to predict future events. Typically, historical data is used to create a mathematical model to capture key trends. This predictive model can then be used for current data to project what will happen next. At the same time, however, measures can also be proposed to achieve an optimal outcome.

Based on the achieved outcome, companies can gain deeper insights into trends and patterns regarding their employees, customers and competitors in the market. Where risks can be mitigated, success and certainty for predictions can be gained at the same time. Current data from various channels, including emails, files, CRM applications, relational databases, social media, and more, is collected and analyzed.

Due to increasing competition, companies are looking for advantages to offer products and services in crowded markets. Using such data-driven forecasting models can help companies achieve more positive business results and solve long-standing problems.

How AI differs
AI has existed for quite a long time, but machine learning is actually being developed.

Machine learning – an AI technique – counts as a continuation of the concepts of predictive analytics, but with one major difference: the AI system can make assumptions, test and learn by itself. AI is a combination of several technologies, and machine learning is considered one of the best known techniques for gaining deep data insight.

In machine learning, algorithms are „fed“ with data and then asked to process this data with prescribed rules. Predictive analytics is the analysis of historical and existing external data to reveal patterns and relationships.

Machine learning works by combining large amounts of data with iterative processing and intelligent algorithms so that the software automatically learns from patterns and relationships in the data.

Different application scenarios
A practical example of predictive analytics vs. AI is online retailers. They use the search and buying habits of their customers to predict the next likely purchase of a customer (Next Best Offer). Based on the prediction, ads and promotional e-mails with suitable products and services can then be placed for the potential customer.

Predictive analytics can also help to avoid churn in the customer base by identifying the customer segments that pose the greatest risk of leaving (churn prediction). Based on this information, appropriate measures can be taken in time to satisfy the customer.

In addition, predictive analytics enables marketing to be optimized in order to attract or retain the customers that offer the greatest life cycle for a company (customer value prediction).

Predictive analytics can also provide suggestions as to which products or services can be combined to increase customer value and revenue opportunities (up- and cross-selling offers).

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Predictive analytics has become an important key prerequisite for deriving important information from data regarding customer needs and actions.

Predictive analytics is used in online shops and streaming services, among other things. Intelligent recommendation algorithms put the focus on individually tailored products or services for the customer or user.

On the right track with predictive analytics
Another trend is the use of predictive analytics in project management. Here the methodology can be used to keep projects within a given scope, time and budget. But also the constant insight into whether the project is on the right track to achieve the previously defined goals can be an important argument for its use in project management.

High potential for Big Data
However, there is often a lack of willingness to use large amounts of data to support decision-making. But in project management there is a high potential for such big data analyses, as the amount of project-related data is increasing rapidly. Cloud storage is also becoming lighter and more available. Partly free of charge solutions create ideal conditions for deployment scenarios.

Data input for new findings
In principle, all data sources serve as input data for the use of predictive analytics in project management. With the help of text mining, even data that is available in unstructured form can be used. The proportion of unstructured data, such as e-mails, log files, images, etc., is far greater than that of structured data. Natural Language Processing can make this data categorizable by reducing it to the most important core information. Mood analysis and information from external sources such as social media can also be integrated.

Based on the recognized patterns and dependencies of this data, new insights can be gained in project management, so that processes can be made more effective and efficient and important recommendations for action can be derived.

Machine Learning as planning support
With the help of machine learning and the predictive analytics based on it, companies can generate many advantages. Project managers receive valuable planning support and make decisions based on quantifiable data, because gut feeling no longer has any place in predictive analytics projects.

Success through preventive action
By using predictive analytics, project managers can act preventively instead of limiting damage. Potential problems can be prevented by identifying undesirable developments early on. In this way, planning success and quality are positively influenced. To this end, the real-time algorithms automatically improve with each further progress of the project.

On the basis of the information obtained in real time, it is possible to see at any time what is happening in the project and why, and to anticipate how the project will develop in the future. For this to succeed, managers must be prepared to actually use the amount of data for decision-making.

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