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 Deploying freed-up resources in sales erschien zuerst auf Dastani Consulting.

Optimizing sales allocation
AI-based segmentation is able to take into account the potential of customers. To do this, the entire customer base is clustered into A, B, and C customers. Then, the customers from the C segment that have the potential to become an A or B customer can be identified. According to this segmentation, these customers are suggested to the sales team for intensified processing. Conversely, the sales team also comes to the conclusion of which customers should no longer be closely managed. Using this AI-based methodology, sales resources can be optimally allocated and distributed according to their best opportunities.

Estimation of objective sales potential
Dastani Consulting’s intelligent potential forecasting develops a functional relationship between external data – such as number of employees, external sales, industry, trade show catalogs or customer website data – and a customer’s revenue. As a result, this AI-based methodology is able to estimate the objective revenue potential of each customer.

Growth of more than 100%
As a consequence, the sales department is pushed to those customer companies that are highly important for the company’s revenue growth. Often we at Dastani Consulting find relevant growth potentials within the customer base amounting to more than 100%, which is distributed over only three to four percent of the address base. Exactly these addresses can then be specifically addressed by the sales department in order to successively conquer the existing potential. Appropriate campaigns can also be planned and implemented accordingly.

The approach outlined here can also be carried out for individual product groups, as it is not only based on total sales. In this way, the system can also continue to identify important cross-selling potential within the existing customer base, which in turn can be exploited.

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 Potential-oriented segmentation 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.

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 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.

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 Use sales resources in a more potential-oriented way 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.

In previous posts we have described the background and benefits of Predictive Sales. Now we answer the question how these possibilities can be implemented in sales. From past projects we have identified three success components for a sustainable increase in sales: The appropriate industry, a pilot test and continuous learning, as well as adaptation of sales processes.

The use of AI in sales is suitable for all companies with a large number of customers with regular demand for products and services. This means specifically in the B2B environment: automotive suppliers, construction materials and tools, chemicals, electronics, wholesale, mechanical engineering, medical technology, metals and raw materials, logistics, IT, telecommunications.

A pilot test is suitable as an introduction to the use of AI, to get to know the procedure and applicability. For example, the probability of success with target customers in the past year can be compared with the actual acquisition success. Or the cross-selling probability with customers of the past year can be compared with the success of an actual cross-selling campaign. Basically, AI learns something new and delivers more reliable results with increasing repetition.

The own data quality does not play as big a role as perhaps expected. Since the ERP system reliably stores data on customers and their transactions, all relevant data for an internal comparison (cross/up-sell, churn) can be drawn from it. For Purchase Probability and Share of Wallet, the corresponding websites must be entered, AI then determines comparable companies on its own.

To make the best possible use of the newly acquired knowledge, the sales management should refresh some classic procedures.

First, the segmentation of new and existing customers should be updated. Today’s turnover, total potential and purchase probability are the most important dimensions here.
The contact strategy for each customer segment should then be defined: Who contacts which customer via which channels and how often with which messages?  The „who“ can be filled in very differently: Key Account Managers (KAMs), field service, telesales or marketing automation solution.
The design of the customer segments affects the organization: Number of KAMs, field service, telesales. A reallocation can ensure that each customer is served in the best possible way depending on his potential.
A daily action plan can be created for each sales representative. This will be different for the home office during the corona pandemic than afterwards. It can be flexibly adapted depending on different visit restrictions.

The examples presented here show how AI can provide sales staff with precise planning tools for targeting, acquiring and developing customers. New customers can be acquired more efficiently and cross- and up-selling measures can be defined accurately. The overall potential of each customer can be identified. Customers willing to change can be prioritized, addressed and retained.

In these issues, AI works with a reliability that leads to a significant increase in sales and margin after only a short period of use. The methods can be applied even in corona times and are promising for every company with active customer approach. Many companies have taken a crash course in digitalization as a result of the pandemic. Companies that extend these findings to the use of AI secure themselves an excellent starting position in the „New Normal“.

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 Concrete use of AI in sales erschien zuerst auf Dastani Consulting.

Despite the massive corona-induced contact restrictions, in which established sales activities have come to a virtual standstill, sales organizations must not miss the post-corona upswing. Companies must therefore prepare themselves optimally by systematically planning their sales activities and play the digital card even more than before.

Digitally organized Customer Journey
At the latest now, after the hard time of the crisis, customers are on a digital customer journey. Customers are making their purchase decisions without ever having communicated with sales. The first point of contact is usually a provider’s website: every click leaves data traces that companies and sales can view and analyze. These Big Data analyses improve customer access. Companies are able to create detailed customer segments and tailor their products more precisely to customer needs. The customer approach can thus be made more targeted.

The sales force of the future
The sales department of the future will pick up customers where they want to be picked up. Product-centered thinking, which focuses solely on the deal, would be too short-sighted, which would not help in the future. The sales force of the future will think more strategically and across departments, no longer just in terms of turnover and deals. They try to make the added value of their company and products visible to potential and existing customers – through knowledge, advice and concrete solutions.

Outlook: Sales Intelligence
According to Corona, it will become apparent how quickly companies and their sales organizations can cope with the drastically changed purchasing behavior of their customers. The longer the crisis lasts, the more likely a complete realignment of sales will take place in some industries. This will be supported by technology-based sales intelligence with close cooperation of the marketing department.

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 Corona pandemic: The future of sales erschien zuerst auf Dastani Consulting.

The artificial word „churn“ is a fusion of the words „change“ and „turn“: the customer changes his behavior and turns away from the previous provider.

An AI system can autonomously find, among all possible criteria, those that actually indicate a willingness to change. Churn management at the data level begins by selecting those customers whose real sales are significantly below the expected value. The expected value is derived from the Customer Value Prediction. In a second step, we evaluate the available sales: if a customer migrates with a probability of 10 percent and we expect sales of 100,000 euros, the risk potential is 10,000 euros. A sales expectation of 20,000 Euro with a fifty percent probability of churn carries the same risk potential, also 10,000 Euro.

The need for action therefore results neither from the probability of migration nor from the current turnover alone, but from the risk potential. It is not worth classifying someone with only 200 euros in sales as at risk and paying special attention to them in the form of expensive customer loyalty measures. It is mainly decisive to address the valuable and endangered customers in such a way that they remain loyal to the company. The success of the applied loyalty strategies can be measured with data analyses. Based on the effectiveness of individual packages of measures, these can then be further optimized.

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 Churn Management: Not every goodbye hurts erschien zuerst auf Dastani Consulting.

Customer Value Prediction shows what can be derived from past orders. Looking to the future, share-of-wallet analysis shows the customer’s full potential. It supplements the customer view with external information. Especially for customers with low sales, it is worth taking a closer look at their sales potential. The share of wallet defines the share of your own sales with a customer in a particular merchandise category as a proportion of the customer’s total budget for this segment, thus showing the maximum amount of sales that could be achieved. The method often reveals hidden potential that exceeds the current sales volume many times over. Well-known B2B companies achieved rapid sales growth by analyzing existing business relationships for further potential and focusing on the relevant addresses.

AI evaluates all available external information about the respective client company, for example industry, number of employees, extracts from the commercial register, annual financial statements, etc. The system uses this information to calculate a sales estimate. The expected turnover is only a part of the total potential: A discrepancy between purchase volume and general market potential indicates that the business can be expanded considerably, since the buyer purchases a significant proportion of the products in the range via a competitor.

The share-of-wallet provides information about the potential that lies dormant in the inventory and how many of the customers are worth addressing more actively. The example of one of our clients illustrates the procedure: An online retailer achieves margins of 5 to 80 percent depending on the product range. However, only 2.5 percent of the 400,000 existing customers in the business sector buy the highest-margin products. We developed a model that forecasts expected values for the various product areas for each individual customer. For the interesting segment of high-margin products, we looked at the customers already buying in this segment. In a next step, we used external data, the websites of these companies and other information to forecast for each customer the probability that they would buy a high-margin product and the revenue that could be generated with it. In this way, we were able to identify a large number of very interesting addressees in the customer base. The share-of-wallet analysis enabled us to invest sales resources in a targeted manner in those customers who most likely still have open potential.

In another case, our value predictions decide which customers the mobile sales force is allowed to visit at all. It is not uncommon for many companies with limited sales and potential to be on the appointment calendar. Such customers can be assigned to telesales after the analysis. In return, the salesperson receives addresses that may currently have little or no turnover, but have great sales potential. This targeted allocation increases sales at constant costs.

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 Share-of-wallet analysis: Discover the potential of customers 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.