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.

IT companies are overwhelmed by masses of leads/requests via social media or other online advertisements. The number of requests is increasing enormously – but not every request is actually good. Often many companies ask – but in retrospect the costs are too high or the offer is not suitable. Roughly estimated, only about 20% of the company inquiries turn out to be useful. Not all enquiries can be processed intensively because sales resources are very limited – often there is only little staff available or the sales department is generally too busy.

But how is it even possible to keep an overview of the actually useful inquiries, which at best lead to an offer being closed?

We would like to answer this question by means of a project with a software company in a special niche, which cooperates with many major international customers.

The problem of the IT company was that the managing directors were complaining about sales regarding lead processing. Due to the high amount of unfiltered inquiries (approx. 1000 per month), the sales department was overstrained to process every single lead intensively and decently. This overstraining was in turn reflected in a high level of demotivation at work. If the quality of the enquiry was only average, the sales staff assumed that nothing would „get around“ anyway.

Predictive analytics for the quality of the leads
For this reason we at Dastani Consulting have developed a procedure – or rather a predictive analytics model – to qualitatively classify a request in advance. This model takes all information about different companies from the web (e.g. industry, employees, turnover), as well as from different reference databases. Subsequently, the interested parties that have been approached are crawled. Based on the historical inquiries, deals and opportunities that have been set, the system is able to judge which of the prospects actually become customers. The AI system learns from the past and can therefore realize considerable learning curve effects.

Using the AI tool
The software company uses the AI-based tool to check older leads on the one hand, where it is still worthwhile to tackle them after a few months. On the other hand, the new requests can be checked every month in a continuous process. Every month, the AI system should decide a priori whether the new request should be forwarded directly to the sales department for intensive processing or whether it should first be answered by office staff, social media or even automatically.

Result: High hit rates
As a result, it turned out astonishingly that the AI system is able to identify a priori with a high hit rate the addresses at which customer inquiries have developed or an opportunity, i.e. ultimately an offer, has been created. In this way it was possible to differentiate between „good“ and „bad“ prospects. Rather, the factors that ultimately turn a prospect into a customer were also identified. It is precisely these customers that the sales department has to process with the highest priority.

Conclusion: Focus on the important customers
This means that by introducing the predictive analytics model, sales can focus on the really important customers. Thus, the sales resource remains constant, the probability of closing deals increases and efficiency increases. Last but not least, everyone is happy and even the prospects who had an inquiry are happy because they got exactly what they wanted.

If you have any further questions, please visit our social media channels (Xing, Linkedin, Instagram) call us at +49 (0)641 984 46 – 0.

Der Beitrag More turnover through focus 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.

In the meantime, not only historical transaction data from completed transactions or customer contact history data from CRM systems are available, but also additional sources (e.g. websites, social media) are of great importance. Intelligent cloud solutions allow access to usage data in real-time mode and offer potential for more flexible performance.

With Big Data to Success
Digital behavior patterns of existing and potential customers can be used through the intensive use of all online channels and social media platforms of companies. As a result, the data pool is growing. We’re talking about 2.5 trillion bytes per day worldwide (equivalent to 36 million iPads of storage capacity). The use of predictive analytics is indispensable for analyzing the data.

Next Best Offer: See what’s next
Intelligent algorithms are able to automatically recognize customer usage and behavior patterns. The activities of a customer can be systematically observed and evaluated. In addition, it is possible to anticipate trends. In sales and marketing, automated forecasts can be used to see what the customer will buy or demand „next“ (Next Best Offer).

The principle of the Next Best Offer (NBO) is the same as with the popular provider Netflix: The methodology of the film and series recommendations can also be translated into the B2B business at the same time.

Here, on the basis of the individual orders, purchases and other data, it is predicted which customer will buy which product next. The targeted customer approach optimizes business processes. Cross-selling potentials can also be tapped in this way.

Forecasts with a high hit rate
The NBO forecasts of Dastani Consulting had in the past hit rates of 42.90% for an online mail order company with over 10,000 products. For example, 3,089 of the 7,200 active customers bought at least one individual recommendation in a given period. This corresponds to almost half of the online retailer’s total customer base. With a rate of 17.35%, customers even acquired more than one recommendation (at least two products).

In comparison – without the NBO forecasts – the hit rates are very high because the online mail order business has a fairly large product portfolio and it is very difficult to find an attractive product for the customer among a random product selection. This means that the larger the product selection in the online shop, the more you as a supplier can benefit from the Big Data analyses.

Conclusion: More opportunities than risks
The digitalisation of B2B sales presents a major change, but the opportunities outweigh the risks. Companies can benefit from Big Data and therefore sales staff must also learn to trust the world of data because they are ultimately responsible for sales decisions. At present, sales is still a long way from „machine selling“, in which machines take over all tasks, but the future belongs to them.

If you have any further questions, please visit our social media channels (Xing, Linkedin, Instagram) call us at +49 (0)641 984 46 – 0.

Der Beitrag See what’s next – Big Data Analytics erschien zuerst auf Dastani Consulting.

Many companies apply CRM systems but use the existing data inadequately in identifying and exploiting turnover potentials. But algorithms driven by AI (Artificial Intelligence) enable the prediction of individual purchasing behaviours, so that data that is lying dormant in a company’s CRM system can be turned into a valuable information source.