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

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

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

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

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

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

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

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

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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.

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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.

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.

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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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Methodology
AI methods determine the actual sales potential of a customer using external data such as industry, employees and website content. The sales potential is calculated by using comparable sales of customers with an identical profile. This is how companies are identified that, based on their industry and number of employees, look as if they would, for example, achieve an annual turnover of 150,000 €, but do not do so. These companies must be addressed in a targeted manner.

Knowing the total potential of a customer
The concept of the Share-of-Wallet is about how much a customer can spend for a company in a certain product area. For this reason, it is not the past purchases in the company that are relevant for the forecasts, but rather the future potential. The share-of-wallet (value) accordingly describes the difference between the total potential of a customer in a certain product group and the actual turnover of a customer in the respective product range.

Result
The Share-of-Wallet forecasts make a significant contribution to the revenue exploitation of existing customers. This is often a considerable problem in controlling marketing and sales. The reason for this is that the actual total potential of the customer is unknown to the company. Sales is allocated to the unused customers in individual product groups by the forecasts. Thus a purposeful focusing on customers with high Potenzial can be obtained. In marketing, too, the share-of-wallet represents a situation that can be used to one’s own advantage with the right information.

The aim of the share-of-wallet is to find out which customers are worth reactivating or expanding in order to realise growth potential in existing customers. Through the predictive analytics applications of Dastani Consulting, revenue increases of 50% can be achieved through targeted reactivation. Discover your sales potential and convince yourself of our tools.

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The Share-of-Wallet forecasts make a considerable contribution to exploiting the sales potential of existing customers, because this is precisely a frequent problem in the control of marketing and sales, because the company does not know the actual total potential of the customers.

Share-of-Wallet Potentials
The Share of Wallet indicates the difference between the total potential of a customer in a certain product group and the actual turnover with this customer in the respective segment.

Objectives and methodology
The goal of a Share-of-Wallet forecast is therefore to find out whether inactive or low-volume customers have additional sales potential beyond the realised sales – and which of these customers are worth reactivating or expanding?

Methods based on artificial intelligence and external data – such as industry, employees or website content – are used to determine the actual sales potential of a potential customer.

The sales potential is calculated by using the sales of comparable customers with identical profiles. In this way, companies can be specifically identified which, based on their industry and number of employees, look as if they would, for example, achieve an annual turnover of 100,000 €, but do not do so. These customers must be addressed in a targeted manner.

Result
The predictive analytics software thus offers a solution where important customers from the B and C customer base with „A potential“ are to be identified in the future and channel segmentation into A, B and C customers is to be carried out in a potential-oriented manner.

The result of these predictive analytics forecasts is the allocation of sales to untapped customers, also in individual product groups, with a targeted focus on customers with high future potential. The sales force can rely on this to generate promising potential.

In marketing, the Share of Wallet represents a situation that can be used to one’s own advantage with the right communication and information. It can be assumed that the „missing“ sales will be made with the competition.

Sales now has the task of finding out why another company – i.e. the competition – is preferred or why one’s own company is not chosen for all products in this product area. The obvious key to success: Dialogue with the customer, because the potential customers of the competition tick like their own customers.

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Der Beitrag Share of Wallet: Open purses with forecasts erschien zuerst auf Dastani Consulting.

Sales departments are flying blind if they don’t know the turnover potential of individual customers in specific sectors. More important than the historic business volume generated by one’s own company is the general sales potential, from which the Share of Wallet can be derived – and systematically raised.

Share of Wallet identifies the difference between a customer’s total turnover in a specific product category and one’s own turnover with the customer in this segment. This provides a very precise measure of sales performance.

•The ideal situation: Mr. and Mrs. Jones are comfortably off and regularly purchase items from all product categories at their local delicatessen. In this case, the retailer is able to exploit his customers’ purchasing potential almost to the full. The turnover achieved corresponds to Customer Value.

•The crisis scenario: In a change of routine, Mr. Jones only looks in every now and then in search of special offers. Alarm bells sound at the delicatessen: the actual turnover no longer reflects the Customer Value of Mr. and Mrs. Jones. They are threatening to drift away.

•The challenge: Mrs. Jones regularly buys pasties and sausages at the delicatessen. However, she gets her wines and spirits from a nearby winery and regularly places online orders for specialities. The Share of Wallet could be significantly increased, but this turnover potential is still in the hands of the competition…

•The almost perfect scenario: Mr. and Mrs. Jones are regular customers and frequently buy delicatessen products ranging from exotic fruits to shellfish and crustaceans as well as chocolates and liqueurs. But the enthusiastic hobby chefs hardly ever purchase anything from the kitchenware department. This promises a strong cross-selling potential.

Our delicatessen retailer knows his customers and intuitively judges the potential correctly. But in bigger stores, the Share of Wallet is determined using AI-based Predictive Analytics software. This involves relating the turnover expectation to the turnover potential, whereby the latter is determined via externally transferable information that has nothing to do with past transactions. Each individual consumer is allocated to a group that behaves in a similar way. For every individual customer relationship, this solution determines how the company can achieve the best result.

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