Sales per customer, sales per visit
To generate a basis for visit allocation, revenue per sales visit can be modeled using the Customer Value – and Visit Value – Prediction we developed. First, the general Customer Value is calculated for each customer in a two-step process. This allows the sales expectation for each customer to be viewed over a previously defined period of time. This Customer Value value is then linked to the Visit Value, the expected revenue per visit.

Visit forecast and potential forecast
Our potential forecast is a helpful supplement to the Visit Value Prediction. The combination of both forecast values enables you to allocate visit capacities efficiently, in a resource-saving and objective way. The following matrix shows a case study of one of our customers and maps a total of 24,000 sales visits. Here it can be seen that if the objective sales potential is high, resources should be invested in reactivating inactive customers and in acquisition and conquest. In contrast, when revenue potential is low, visit resources should be reallocated. In this case study, 5,000 customer visits are affected and should be handled via telesales rather than in person. This represents a total of about 20% of the total visit resources and illustrates the relevance of resource reallocation. If both the objective potential and the visit forecast ratio (VVP) are high, consideration should be given to increasing the visit frequency. By means of the various forecast values, this gives you the opportunity to better assess your customers and tailor their care to them in a goal-oriented manner.

Finding the needle in the haystack
In numerous customer projects, we have found that companies often prioritize their customers according to historical sales. Accordingly, the focus of their sales staff is on the „good“ and active customers. However, this approach is not target-oriented, as the revenue potential of your customers cannot be fully exploited in this way and some customers are visited too frequently. This is where we apply our Customer Value – and Visit Value – Prediction in a target-oriented way for you, so that you increase the sales efficiency of your sales team and exploit the enormous potentials. Our tools ensure that you visit the right customers and effectively implement sales and marketing measures. You get an overview of the objective sales potential of your active and inactive customers as well as new customers. Using an additional top-down list, you can see which customer should be visited first by your sales team in the coming week. With the help of these key figures, you can finally find out where you need to focus your sales optimization efforts.

Our numerous case studies illustrate the success of these measures. 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 Sales optimization in B2B erschien zuerst auf Dastani Consulting.

Measures of sales prediction
The Corona crisis has made it clear to all of us that sales activities can work effectively despite everything. A number of measures need to be considered to achieve this. First of all, the sales resources must be re-optimized, because not every sales visit is worthwhile. Instead, the expensive visits must be focused on customers with high sales expectations.

But which customer has which sales expectation? Various predictive analytics forecasts are used for this purpose. Based on AI, they are able to forecast the sales of each individual customer and the expected values of the individual sales visits. The appropriate sales strategy emphasizes a better allocation of resources – when is a sales visit really necessary and when should the phone be picked up?
Drive each customer’s sales recommendations across all channels – what should the customer buy now at this point in time? By saving sales resources, important potentials can be conquered.

Use of the CVP and VVP
Our Customer Value- (CVP) and Visit Value Prediction (VVP) create the basis for the allocation of sales visits. While Customer Value Prediction forecasts the expected sales of a customer for a specific product group, Visit Value Prediction predicts the expected sales of a visit per customer. On the one hand, this allows the general customer potential for the next period to be evaluated and, on the other hand, visit and tour planning can be optimized by sales allocation.

Optimization of the contribution margin through the visit prediction
Many sales employees claim that in retrospect they are always smarter about which customers a sales visit was worthwhile. But the Visit Value Prediction we have developed is able to predict in advance which 70% of sales activities will be completely inefficient. These inefficient sales activities do not lead to sales, as they are associated with a negative contribution margin. Visit Value Prediction makes it clear that in the future, it will often not always be necessary to visit the same customers as in the past. The high proportion of sales resources freed up can in turn be used to acquire new customers and reactivate customers with high sales potential.

Assessing a customer
But what options are now available for assessing a customer and tailoring the service to that customer? On the one hand, there is the objective sales potential, in order to answer the question of how high a customer’s utilization is. On the other hand, the Visit Value Prediction serves as an opportunity to see which customer should be visited first next week, ranked by the top-down list that has been created. For this purpose, the Potential Prediction optimally complements the Visit Value Prediction (see figure). The combination of these two key figures enables a sensible, resource-saving and objective distribution of visit capacities.

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Der Beitrag Almost 70% of sales activities completely inefficient erschien zuerst auf Dastani Consulting.

In a survey conducted by Deutschen Industrie- und Handelskammertag e.V. (DIHK), 77 percent of the approximately 8,500 companies surveyed stated that they expected a decline in sales in 2020 as a result of the coronavirus. In contrast, 5 percent expect an increase in sales (source: Statista).

Many companies are not yet aware how they can successfully benefit from AI technology. AI works with a strong reliability, which leads to a significant increase in sales and margin even after a short period of use – even in times of corona. In order to secure a secure starting position in the New Normal, companies should expand the use of AI.

Forecast of the expected turnover
What is the sales expectation of my customers? Customer Value Prediction was developed to determine the sales expectation for a certain period of time. The value of a customer (Customer Value) is derived from past transactions. Based on this information, the system is able to forecast the sales of a customer in the next period.

Forecast of the expected turnover of a visit
And what is the sales expectation for my next sales force visit in the following week? To answer this question, the general customer value determined in the first step is linked to the visit value. Sales and visits from the past serve as data input.

Using resources more potential-oriented
Consequently, it turns out whether a sales force visit in the next week is worthwhile at all. Visit Value Prediction often confirms that companies invest too many sales resources in customers with low sales expectations. This turns out to be uneconomical – but sales can counteract this by greatly reducing non-economical field sales force visits and substituting them with telesales, for example. At the same time, Customer Value – and Visit Value – Prediction frees up unprofitable sales resources, which in turn can be invested in high-potential existing customers, in the acquisition of new customers and in the reactivation of customers with high potential.

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Der Beitrag Use sales resources in a more potential-oriented way erschien zuerst auf Dastani Consulting.

Each sales employee is responsible for several hundred inventory addresses. However, he cannot intensively support all customers in the course of a year. In most cases, the salesperson therefore intuitively decides on the addresses of the customer base with which he has been able to build up a special relationship and with which he can easily generate sales. As a rule, these are also the strategically important customers. However, it would be uneconomical and often not even possible for capacity reasons to have the remaining customers processed by the sales department.

Evaluate with Customer Value Prediction performance
Dastani Consulting developed the Customer Value Prediction model as a solution approach to identify the most promising customers from the large pool of the masses and to recognize acquisition decisions as early as possible, so that the sales department is actively involved. In Customer Value Prediction (CVP), the entire customer base is evaluated with regard to the future performance of each individual customer and thus its importance for the company. The aim is to predict the future revenue, cross-selling and up-selling potential of each individual customer.

Evaluation of transaction data
The CVP is primarily based on the transactions stored in the ERP system and thus the company’s own data. The system learns independently based on several million transactions of the past years. Statistical formulas are derived from this data to determine whether a customer from a certain product group will buy within a defined period of time (e.g. within the next 12 months) and in what amount of sales he will be active.

Customer-specific expected values
As a result, the customer values per customer and product area are output for a previously defined period of time. The following figure 1 illustrates the degree of segmentation in a respective product area, which is very striking. For marketing and sales activities, only the first 10% of customer addresses are usually of interest.

For an example customer, the sales expectations can be broken down by product area. Adding up all product groups results in the total sales forecast of the customer. It indicates the customer’s value contribution to the future development of the company.

Benefits of the forecast
The CVP can be used in a variety of ways as the basis for centrally controlled communication. In the so-called needs analysis, customers can be addressed with the most relevant products. On the other hand, the customers with the highest affinity for a certain product can also be selected. In this way, certain product areas can be promoted, sales campaigns can be addressed and year-end business can be boosted.

Application for sales allocation
In principle, other possible applications are conceivable: Customer value prognoses make it possible to address customers specifically according to their preferences.  The analysis identifies the customers in the C segment that have the potential to become A and B customers and can be suggested to sales for more intensive processing (Figure 2). Conversely, however, it is also possible to identify which customers should no longer be closely monitored by sales. The segmentation of customers into A-B-C customers can be corrected on the basis of the analyses (Figure 3).

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Der Beitrag Customer value-oriented segmentation erschien zuerst auf Dastani Consulting.

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.

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 B2B Marketing and Sales in St. Gallen erschien zuerst auf Dastani Consulting.

Determining the expected turnover
AI methods have gained considerable added value, at least since the severe crisis situation. The first step is customer value prediction, which makes it possible to forecast the expected turnover of each individual customer for a specific period of time (e.g. for six months). The model derives the value of a customer from past transactions. In this way, the system predicts what turnover a customer should achieve in the next period.

Use of Visit Value Prediction
After determining the general customer value (sales expectation) for each individual customer, this can then be linked to the visit value (sales expectation per visit). The visit value prediction predicts the expected turnover for the next visit, e.g. in the next week. In this way, the visit value prediction can also indicate which visit will turn out to be uneconomical and would be associated with a negative contribution margin. The model is based on past sales and visits.

Sobering results
However, it is not uncommon for visit value predictions to conclude that companies invest too many sales force visits in customers who have low sales expectations. Sales can counteract this by reducing the amount of non-business field sales force visits and replacing them with telesales, for example.

Putting the predictions into practice
Both the customer value prediction and the visit value prediction are able to support the sales department significantly and to use the resources in a more potential-oriented way. The sales department realizes how many and which visits can be better allocated and how much potential its active and inactive customer base offers. In addition, the sales department can identify how well the potential is already exhausted for which customer and focus on unused potential. Released sales resources that are not profitable according to visit value- or customer value prediction can be used differently.

The success is evident
The customer-specific forecast values determined can then also be aggregated to the sales employee level. The performance of all sales visits can thus be objectively assessed. Using the top-down list of the visit value prediction, the sales staff can see which individual customer is to be visited.

Our strategy is to use our forecasts to identify uneconomical visits and reduce the frequency of visits. Furthermore, we invest resources that have been freed up in conquering high-potential customers, in acquiring new customers with high potential and in activating customers with the greatest potential. We also forecast the potential. We estimate that these measures will enable us to increase sales by up to 20% – without having to build up additional sales resources.

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 Visit Value Prediction | Predictive Analytics erschien zuerst auf Dastani Consulting.

Besides new customer acquisition, the development of existing customers is essential for any B2B sales force. Usually, a deep understanding of the customer (Customer Insight/Intimacy) decides which other products or services could be relevant. Here, too, AI provides support, and with significantly less human evaluation of existing customers than in previous cross- and up-selling. Companies only need to know what turnover each customer has made and what they have bought from them. AI is then able to forecast the turnover of each individual customer very accurately for a specific period of time. The method determines the customer value for each product group and, if necessary, even broken down to each individual product. We can therefore know which turnover is possible in which areas and which products interest the customer. A comparison with the business the company has done with the customer so far shows the cross-selling and up-selling potential. It often happens that this new kind of transparency gives the sales department an indication of products that they would not have considered for the customer themselves.

Due to the corona pandemic, the sales force is currently still restricted in its ability to visit customers. Suddenly the contact strategy is not characterized by geographical proximity. Sales staff or customer service representatives can approach all addresses equally. This is where AI makes a valuable contribution by deriving requirements from past orders. This information can be used across all channels – from telesales to video calls from the home office to ready-made order suggestions in the online shop. In addition, the system quickly learns how an unforeseen event such as Corona affects customer behavior. This has enabled large sales organizations to reallocate their sales resources very flexibly in recent months.

It is extremely informative to compare actual customer behaviour with the forecast values. If the realized sales remain far below the expected value, the question arises why the customer did not buy the predicted products. Valuable impulses can be derived from this. For example, it is quite possible that salespeople have achieved the agreed targets, but 20 or even 30 percent of customers should have performed better. If you dig deeper here, you will sometimes come across structural weaknesses: Products were not even offered in the first place, a sales area is not or not sufficiently staffed, or it was not possible to retain a customer – he has long since migrated to the competition. If companies can intervene early on thanks to sound analyses, this strengthens the sales organisation and promotes a sustainable increase in turnover.

This article is part of a series on LinkedIn about #PredictiveSales:

1. the potential lies in forecasting

2. technical requirements

3. forecast the purchase probability of potential customers

4. identify cross- and up-selling potentials

5. discover the turnover potential of the customers

6. not every goodbye hurts

7. concrete use in sales

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 Predictive sales – cross- and up-selling potential erschien zuerst auf Dastani Consulting.

A sales department without customer visits? The contact restrictions caused by Corona posed particular challenges for the sales organizations. The most frequently asked question in sales force planning before the Corona crisis was: How can most customers be reached by the shortest route?

Instead of personal on-site visits, companies had to rely on digital methods. But what happens when the location of the company no longer matters and business is suddenly done from all locations around the world? This is exactly what the Corona crisis has shown us by showing us that sales work can be more effective.

A look at the details
Many companies now look at their sales organization in more detail than before the Corona crisis. Particularly interesting are the revenues generated by telesales during the crisis. However, it is not only past sales, but also the sales potential and sales expectations that determine the value of a customer. The predictive analytics tool Customer Value Prediction, developed by Dastani Consulting, is able to forecast the sales of each individual customer for a certain period of time. At this point, AI makes a valuable contribution by deriving the requirements of orders from the past. This information can be used across all channels. The system learns very quickly how an unforeseen event such as Corona will affect customer behaviour.

Value Predictions
Customer Value Prediction shows what can be derived from past orders. The Share-of-Wallet Prediction is directed into the future and shows the full future potential of a customer. Especially for customers with low sales, it is worthwhile to take a look at the sales potential. The share of wallet (value) is defined as the difference between the total potential of a customer in a specific product group and the actual sales of a customer in the respective product range. With the help of Share-of-Wallet Prediction, sales resources can be invested in those customers who still have open potential.

Customers with low sales potential and low sales expectations can be assigned to telesales according to our analysis. Again, this means that non-economic visits are reduced and/or substituted by telesales. The capacity of sales resources is minimized, so that in the future these customers will not be served as intensively as in the past. Customers whose sales expectations and sales potential are high, on the other hand, require much more intensive support. This frequency of visits must be increased even more in the future.

Using saved sales time for acquisition
In order to hold their own in the market in the long term, companies must continuously acquire and reactivate new customers. Our predictive analytics process Target Group Predict is used for this purpose to actually acquire those customers who also have high growth potential. Algorithms read the websites of their best existing customers in order to detect patterns and correlations. Several million websites are then compared with them. A software takes care of the degree of correspondence with the target company. Approximately 20,000 different characteristics are incorporated into the forecast model. From this, it is possible to deduce not only the probability with which a company will become a customer, but also the product range for which it has a high level of interest. Individual product lines can thus be developed in a targeted manner. In addition, the sales organization can determine which sales can be achieved with potential customers. Accordingly, sales employees are able to allocate resources in a resource-optimized manner, not only in the field, but also in telesales.

The potential lies in the forecast
But how does a sales representative recognize who he should visit next on his tour? Thanks to the high forecast quality, the sales representative is provided with important contact information without having to pick up the phone first. The sales potential can be systematically exploited, no matter how the sales area is demographically structured. Many B2B companies are already using it to optimize their new customer acquisition. Evaluation criteria such as industry affiliation and number of employees are completely pushed into the background. Since the potential analysis results in a very precise assessment of worthwhile sales visits, direct marketing campaigns and call centers are largely unnecessary. This not only increases the targeting accuracy of sales activities, but also speed and cost efficiency. For this reason, companies are now being asked to rethink their traditional actions, at the latest now after the hard times of crisis.

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 Increasing efficiency and effectiveness in sales erschien zuerst auf Dastani Consulting.

Max Frisch provided a nice definition of the crisis situation: „A crisis is a productive state. You just have to take away the taste of disaster.“ There is something in this definition that many companies overlook: The approach to solving a problem. A crisis is a difficult situation, a turning point in a development, a decision-making situation that is also reflected in the sales organizations. But like all previous crises, the COVID-19 disaster will pass. Over time, a new growth boom will be triggered, based on the changed preferences of customers.

A look into the future?
For some companies, short-term survival is currently the only thing on the agenda. A look into the future? Unimaginable. Other companies, on the other hand, still have some reserves and are considering how to position themselves after the crisis. At least it may be certain for most companies by now that a return to normal pre-crisis normality does not seem to be an option in the future.

COVID-19 as a chance for a new way of thinking
In many places the approach of burying one’s head in the sand when making such a change is widespread. However, it is better to refrain from doing so, as the consequences of the crisis will be felt for a long time to come. Therefore, sales organisations should see the corona crisis as an opportunity and rethink their traditional approach, as COVID-19 has shown that sales can work more effectively.

Sales in times of crisis
Many traditional sales measures and instruments were no longer enforceable due to contact restrictions and posed great challenges for sales: Strong cost pressure, sales slumps, cancellations, lost trips to customers, unstable financial situation of customers, etc. are often among the problems that numerous companies have been confronted with in the past weeks and months. For this reason, many of our client companies are now looking at their sales organisation in more detail than before the Corona crisis. A particular focus is on the sales generated by telesales in recent months. However, it is not only past sales, but also the sales potential and sales expectations that determine the value of a customer.

Customer Value Prediction and Potential Forecast
The Customer Value Prediction (CVP) developed by Dastani Consulting is able to forecast the sales of each customer for a certain period of time. This sales forecast is derived from the past orders of each customer. Our Share-of-Wallet Prediction is also able to show the full potential of a customer. Not the location of a customer on the sales route planning is of interest, but rather the probability and amount of a successful customer deal. This means that sales employees should take a potential-oriented approach when approaching customers.

The Value Predictions make it clear which customers are worth visiting in person after the Corona crisis. According to our analyses, customers with low sales potential and low sales expectations will in future be assigned to telesales instead of personal visits. In contrast, sales capacity should be expanded to include customers with high sales potential and high sales expectations. These customers will have to be serviced even more intensively in future.

Target Group Predict: Acquisition and exploitation of potential
Our intelligent process Target Group Predict is able to evaluate the sales potential of inactive customers and to acquire more inactive addresses that actually have a high growth potential. The sales capacity saved (through telesales) can thus be used for acquisition and potential exploitation.

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 Guide for sales organisations after the Corona crisis erschien zuerst auf Dastani Consulting.

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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Der Beitrag Artificial Intelligence (AI) vs. Predictive Analytics erschien zuerst auf Dastani Consulting.