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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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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Der Beitrag B2B-Sales: Trends for the use of AI erschien zuerst auf Dastani Consulting.

Every minute, Internet users around the world submit almost five million queries to Google, send over 18 million text messages and 188 million e-mails. However, these examples represent only a fraction of the immense amount of data that people produce in a whole day. Say and write about 2.5 trillion bytes a day.
Digitization is increasingly becoming part of everyday life. It is changing the way people communicate with each other and obtain information. As a result, a customer has constant access to a wide range of information. A few clicks are now enough to research relevant facts about products or services. Customers not only know about a provider, they also know what the competition offers.

Understanding customer needs with a 360 degree view
With this in mind, it is more important than ever for companies to create a unique customer experience for the customer. From the first moment of contact to the entire customer experience. Understanding customer needs requires a 360 degree view of the customer. To do this, information about all moments of contact between the B2B customer and the company. For companies, data produced by consumers in their customer existence is of particular interest. Be it the IP address, Google search queries, clicks on the websites, activities in social media or newsletter registrations. In return, however, customers also expect companies to meet their needs in the best possible way and use digital tools to evaluate the data collected. This forms the basis for successful customer intelligence.

What exactly is Customer Intelligence?
The term Customer Intelligence (CI) describes a process for collecting and analyzing customer data. The goal of customer intelligence is to achieve closer customer relationships and a better understanding of customer behavior in order to generate greater business success. This makes it possible to predict future customer behavior based on the insights gained. With the help of customer intelligence, relevant competitive advantages can also be achieved, which is why it is of such great importance in companies. Customer Intelligence can be used to save costs in relevant areas or to increase customer satisfaction, which ultimately translates into higher revenues.

Data and data source for Customer Intelligence
Companies can generally use any system that collects data from customers and prospects as an internal or external data source for the CI process. Internal data sources include the database that contains customer information (e.g. address information, orders, etc.), but also CRM applications (which include customer master data) and web analysis tools that evaluate visitor behavior in online channels. From external data sources, on the other hand, information such as demographic data, activities in social networks or location-based data can be extracted.

Customer Intelligence: A continuous process
In this context, customer intelligence is considered a continuous process in which data must be continuously collected, processed and used. However, it is not a matter of evaluating all data, but rather of specifically analyzing the data that is necessary for an optimal customer experience. This optimized material of data can then be analyzed according to various aspects. Depending on the direction of the analysis, different insights for different business areas can be gained from the collected data.

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Der Beitrag Customer Intelligence: On the customer’s data track erschien zuerst auf Dastani Consulting.

In some B2B companies it fails because of a holistic customer experience. This is due to the different working methods of the sales and marketing departments. While the sales department focuses on the individual customer at the end of the sales funnel and thus on sales success, marketing focuses on the product level and specializes on target groups at the beginning of the sales funnel in order to generate long-term leads. This sales funnel serves as an important tool in B2B to select potential customers through several phases until the business is successfully concluded.

More information vs. high pressure
Digitisation creates completely new requirements: Buyers are more demanding and have more information at their fingertips, while sales staff are under great pressure to present the right product in the right place at the right time. Buyers expect personalized content that is tailored to the specific situation.

Success through collaboration
A successful customer experience can only be guaranteed if sales and marketing pull together. This is the only way to optimize the customer journey in B2B and make it customer-oriented in order to ultimately achieve a positive business result.

In the following, we will look at the reasons for a successful close cooperation between marketing and sales in B2B.

Greater satisfaction
Buyers want to experience the feeling of controlling the entire business process themselves. They are not a fan of crude and obvious sales techniques. Instead, they want to be able to access information independently. Marketing can control this by providing the content that enhances the desired experience for the buyer or prospect. In sales, this ultimately translates into higher revenues.

Strong customer loyalty with cross- & up-selling potentials
It is also important for B2B marketing and B2B sales to build a close relationship and to present themselves as sales experts. This considerably increases the potential for cross- and up-selling, because a regular dialogue takes place. Further suitable products or services as well as extensions can be offered to the customer in a targeted manner.

Faster process
An individually created content shortens the time from first contact to conclusion enormously, as the positively resulting purchase experience leads to an emotional preliminary decision. The right content at the right time is essential for the buying process.

AI helps to carry out important analyses of effective corporate content. In addition, it shows the sales department previous sales situations that led to a successful conclusion. But AI also helps marketing to develop content strategies that are individually tailored to the buyer and decisive for customer acquisition.

Conclusion: With common goals to optimize the customer experience
While the marketing teams focus on generating leads, the sales team is specialized in closing deals. Both work on opposite sides of the sales funnel. However, they are dependent on each other and must develop a common plan. This starts with the identification of ideal customer profiles, on which the relevant content must then be created.

This means that, in order to improve the customer experience in the long term, sales and marketing must be in constant communication with each other. This creates common goals that strengthen the optimization of the customer experience. If both departments recognize the high potential of their joint collaboration, they will be prepared to break down old patterns and invest in new digital AI-based structures.

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Der Beitrag Exploiting the opportunities of digitalization erschien zuerst auf Dastani Consulting.

Increasing AI technologies in the course of digitalisation ensure that the respective needs of a customer are recognised on the basis of generated data. For this reason, for many companies, clear support from AI with reorganized sales teams is at the top of the agenda for 2020 in order to assert themselves in the market against tough competition.

Lead generation through chatbots
Especially when it comes to lead generation, the AI is assigned an even more important role than last year. The use of chat offers helps in addressing customers and filtering interests.

Chatbots are responsible for managing customer communication, analysing comparable lead conversations and preparing offers for potential customers. They serve as a cost-saving alternative. For further processing and qualified customer contact, however, it is still impossible to imagine life without the personal sales representative.

Netflix principle in B2B sales
Since companies have a very large amount of data and gain more information about their potential customers every second, it is important to use this data successfully. This is the only way to achieve a competitive advantage over other companies. By using the AI and self-learning algorithms, the user behavior of customers on the web can be permanently analyzed.

The „Netflix principle“ can therefore also be applied to sales work in B2B. Intelligent systems provide sales staff with information for potential new customers based on past successful customer orders. Furthermore, they recognize patterns and derive optimal recommendations for action.

Target Group Prediction and Next Best Offer
Use the Target Group Prediction developed by Dastani Consulting intelligently to acquire potential new customers. You can increase your lead rates by up to 50% in the B2B market.

Accordingly, with the development of the Next Best Offer you can also offer your customers optimal products by recognizing customer needs and preparing the offers individually. In this way you can increase the probability of closing a deal and strengthen your own position in the market.

The optimal customer experience
Designing the best possible customer journey is the be-all and end-all for the customer to guarantee a successful buying experience. Customer Experience Management therefore serves as the long-term key to success in e-commerce.

Customers demand a strong sales department on the one hand, but also digital channels on the other. Especially at the initial purchase, customers place a lot of trust in the sales staff, for example when it comes to the appropriate advice.

Conclusion: Interaction of artificial and human intelligence
KI improves customer relations and relieves the burden on sales staff. However, the optimal customer experience in 2020 will not be achieved through the exclusive use of AI, but through the interaction of artificial and human intelligence.

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Der Beitrag Distribution in 2020 erschien zuerst auf Dastani Consulting.

KI can make an additional contribution to existing B2B commerce processes in various areas:

Predictions
Because AI is able to analyze a myriad of data and discover unrecognized patterns in it, it can be used to provide the right products and services to the customer at the right time – even before the customers themselves have thought about their own needs.

The identification of demand peaks in relevant markets and the subsequent indication of a suitable offer is also one of the strengths of AI. There are no limits to the creativity of users and service providers at this point, as the B2B business offers a lot of potential for AI applications.

Personalization
The B2B business has a big advantage over the B2C: personalization can be used much better.

Not only the ads of products and services can be personalized for the customer, but also the menu items in the navigation bar of the website. For example, if the potential customer prefers the category „screws“ in an online shop, this menu item can be intelligently placed at the top.

The dream of a B2B customer can be fulfilled through targeted personalization: He can expect a completely personalized shop, which he enters after logging in. The dealer in the B2B business can carry out the personalization because he has a lot of information about his customers and knows the history behind it. This includes, for example, price sensitivity, payment ethics or the knowledge of whether the customer is an A or B customer, which is assessed on the basis of turnover, contribution margin or customer value.

Dynamic pricing
In B2B, prices are a matter of negotiation. They reflect the appreciation of the customer relationship. However, salespeople often take a critical view of AI-based pricing solutions, because in B2B business these are mainly large orders and their customers already have their prices.

Nevertheless, dynamic prices are conceivable if the focus of the method is on small and quick repeat orders and larger orders are not included in the considerations. Another possibility would be so-called commission models. The sales people who transfer their customers to the new online channel receive a commission for this instead of for the individual sale. This could save a considerable amount of time, which in turn could be used to acquire new customers.

Recommendations
Unlike consumers in B2C, B2B customers do not want to spend hours browsing through the online shop. They want to meet their demand quickly and effectively instead of being guided through various navigation points and colourful animated clicks. The B2B business demands an effective and fast way to reach its goal. For this reason, recommendations are nowhere more useful than in B2B.

Conclusion: AI in B2B conceivable as an add-on
The application areas of the AI must be communicated to all departments and responsible persons. Despite the immense passivity in B2B in the past, it is slowly becoming a good time to think about AI in B2B business. AI does not pose a threat, but serves as an add-on that needs to be tailored with a sense of proportion and requires digital support in processes.

In a second part of the article series, you can read about the stumbling blocks that AI can encounter in corresponding projects.

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Der Beitrag B2B Application Areas for Artificial Intelligence erschien zuerst auf Dastani Consulting.

According to good old tradition, countless wish lists are written every year during the pre-Christmas period. At this point, AI tries to avoid unpleasant gifts under the Christmas tree by formulating the wish list itself.

From Social Media Posting to personalized Newsletter
For example, a customer wants some headphones for Christmas. Although she has not yet put her wish in writing on her Christmas wish list, her preferred online retailer already knows, as the customer marked her sister on a social media posting a few hours ago. The online retailer uses this information by placing the headphones in a personalized Christmas newsletter via email to the customer.

Success for the online retailer
Just a few days later, the customer strikes and orders the headphones herself. She even packs a few more presents for her family and friends. By displaying further personalized product recommendations, this is possible without great effort – within a few minutes the customer’s shopping basket is packed with matching products.

Customer satisfaction thanks to AI
After placing an order, the customer can immediately delete several items from her shopping list and already knows before the gift is given that the gifts to her family will be well received.

By bringing together all of a customer’s publicly available information into one profile, the AI turns the entire shopping process into a thrilling experience of success for the consumer.

Analysis of preferences & needs
Using intelligent algorithms, the preferences and needs of consumers in social media networks, blogs, rating portals, etc. can be analyzed. Not only all findings from the Internet, but also the historical buying and search behaviour of customers serve as the basis for individualized product recommendations.
All information is then collected in a CRM system. This enables a 360-degree view of each individual customer.

Individual approach
The better a company knows its customers, the more targeted it can act. This is exactly what the example in the introduction illustrates.

By presenting suitable offers at the right time, the online retailer can score points, especially in the pre-Christmas business. Customers want to be thrilled along their customer journey: From the targeted address at the first contact to the delivery to the door. Particularly during the Christmas season, customers want their shopping to be smooth and easy.

Business development forecast
On the one hand, KI supports the online retailer with an individual approach: because only those who offer their customers products for which they really have an affinity can significantly increase their sales. On the other hand, the AI – in good time before the big Christmas season – also serves to forecast one’s own business development at the end of the year and to identify trends in order to expand the product range accordingly.

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Der Beitrag Automated Christmas presents erschien zuerst auf Dastani Consulting.

Due to the rapidly growing amount of data sets, predictive analytics methods are able to recognize and use patterns in the available data. Not using existing data could be a waste of resources for the organization. For this reason, the methods are used to make the use of resources more efficient and to fulfill the essential functions described in this article.

Tailored Recommendations
A recommendation is only efficient for a customer if it addresses the individual interests and needs of the customer. Predictive Analytics understands the consumer’s buying behavior by combining information from past purchases or current searches to derive the customer’s future actions.

Pricing
The „one price fits all“ times are long gone. This is particularly noticeable when, for example, two people open the same online hotel booking portal at the same time and compare prices. Prices will vary because they are based on the history and search queries of the respective user.

At this point, the magic word for pricing policy is Dynamic Pricing. Online providers can sell their inventories at the best price and significantly increase their profits with increasing product demand by exploiting customers‘ willingness to pay.

If there is insufficient knowledge about the prices of the competition in the market, it can happen that the online shop offers the identical product at more favourable conditions, although the higher prices are also demanded by the competition. This in turn would indicate a strong loss of consumer surplus. With the help of intelligent software support, the pricing policy can be optimized in comparison to the competition.

Vertical value chain integration
Predictive analytics is able to generate added value in various business areas (e.g. warehousing and procurement). By automating orders and returns, the entire process can be made more efficient and cost-effective in the long term.

Business Intelligence for quick decisions
An extremely valuable argument for predictive analytics applications is the ability to anticipate customer expectations and market trends in the future. They can be the driver for more conversions and sales, which is made possible by tailor-made prices and product selection tailored to the customer profile. Understanding a customer’s motivation can lead to a significant increase in sales and to better product placement in the long term.

Improved Customer Experience
The majority of buyers in e-commerce are digital natives. The digital customers of this generation want to see the products they have an affinity for when they open the online shop. The use of predictive analytics is therefore very helpful for potential customers who can spend a higher budget to address them directly.

Algorithms also for smaller companies
In the future, more and more companies will be able to use intelligent algorithms because costs will become lower and lower. The fact that algorithms are learning faster than ever before is extremely positive and indispensable. For this reason, it is important to use the power of forecasts as traders to your own advantage.

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Der Beitrag Predictive analytics in e-commerce: How can online merchants improve their service quality or streamline their processes? erschien zuerst auf Dastani Consulting.

In recent years, customer purchasing behavior has changed dramatically. In the age of digital information and procurement channels, as well as mobile and online channels, traditional sales channels are under great pressure. Customers hope to receive support across all touchpoints of a provider. For a provider, however, the focus is on increasing efficiency instead of relationship orientation.

Sales work is different
Sales tasks are changing: business with standardized products can be handled more effectively in digital form in the future. Via channels such as online portals, social networks, social media, news feeds, apps, etc., customers can be supported throughout their customer journey with positive experiences. For a product that does not require explanation, a customer „only“ has to initiate an order process because all the other information required for the purchase is already available.

At the same time, this means for the sales department that the focus can be placed on potential major customers with products that require more explanation and more complex decision-making structures, because fewer customers need to be personally looked after by the sales force.

The use of the KI
AI applications can help calculate a customer’s likely needs. Forecasts are developed for potential cross/up-selling sales or new products. The potential-oriented customer segmentation available in this way steers resources in the desired direction. The results are sales-promising visit and contact suggestions for the sales department.

Models raise forecasts to a completely new level
We at Dastani Consulting have developed models that raise forecasts to a whole new level and map trends in sales development. The models are based on statistical data and are able to anticipate the behavior pattern of individual customers within a pre-defined time window. The forecasts are extremely precise and can therefore provide optimal recommendations for action for the players involved in market development – sales, office staff, marketing and service.

Fields of application for forecast models
Whether sales forecasts for a product in different target groups or regions, models for dynamic pricing or predictive maintenance for machines and smart services – predictive analytics tools can support you in many areas of your company, especially in data-driven sales planning.

As we at Dastani Consulting have a wide range of predictive analytics applications at our disposal, we are able to implement individual solutions for our customers. With appropriate predictive analytics tools, sales can evaluate and plan concrete sales scenarios and probabilities for the future. With supporting algorithms, it is possible to make predictions about the value of customer relationships and thus better exploit sales potential.

Uncovering undiscovered potential
With Target Group Predict, you can uncover important acquisition potential and win the best new customers in the long term. A Customer Value Prediction can also determine the sales potential of each individual existing customer and thus reliably forecast the Customer Lifetime Value. The Share-of-Wallet forecast finds out for which inactive or weak existing customers a reactivation is worthwhile in order to realize important up- and cross-selling potentials.

Conclusion
The systems of Dastani Consulting discover facts, which are not to be excluded with an optimization of the business results. In addition, they make it possible to uncover previously undiscovered potential in B2B business.

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Der Beitrag Efficient sales management: How companies benefit from predictive analytics erschien zuerst auf Dastani Consulting.

About the model
A churn prediction model is helpful in identifying customers who want to cancel a contract or switch to a competitor, for example. With the help of artificial intelligence it is possible to recognize the impending loss of customers at an early stage and to defend against it with countermeasures.

Intelligent algorithms are able to distinguish annoyed or even ironic e-mails from friendly or factual e-mails. However, customer satisfaction can also be derived from the analysis of the purchase history. If the correct data is combined, the probability of a change can be determined precisely.

For companies, this therefore means that they must increasingly rely on data evaluation. This makes it possible to segment the customers according to suitable criteria and to individualize the address in order to address a customer at the optimal moment. At this point, the model is helpful in determining when a customer is thinking about changing provider.

Application
Depending on the products or services offered, the calculation of the churn rate pays off. The rate is usually determined as the quotient of the number of total customers and the number of customers migrating. Above all, the model is worthwhile for companies that sell goods at cyclical intervals. Examples include automobile manufacturers or mobile phone providers that rely on a subscription structure.

A churn prediction model can be used to measure the impact of individual offers or services on customers. With the help of the predicted probability of churn, individual discount campaigns can be switched automatically. By analyzing the user data, customer understanding can be improved accordingly.

Churn prediction pays off
The costs of acquiring new customers exceed the costs of maintaining existing customers by far. The churn prediction model is therefore worthwhile because the causes of customer churn can be determined and not only the loss of individual customers can be prevented.

The basis for successful modeling is the combination with intelligent customer relationship management. Fast action and appropriate preparation are important when it comes to anticipating customer churn. This is the only way to implement measures to keep customers.

In the long term, the quality of the company’s own product range can be improved and adjusted to the potential customers. The churn prediction model is therefore a central component of a customer-oriented business strategy.

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Der Beitrag Churn Prediction: Avoid customer loss in time with the right measures erschien zuerst auf Dastani Consulting.