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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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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Data Lakes play a decisive role in solving this problem. A data lake is a place where a company can store all structured and unstructured data. Due to the amount of information, the data lake can be used for flexible analyses in the Big Data environment.

The concept of Data Lakes
The Data Lake stands figuratively for a lake full of water. This lake is filled from a steady stream of data such as e-mails, Excel files and content from social media platforms. On the one hand, unstructured data such as raw data such as e-mails, PDF files, images, videos or social media articles are displayed, which do not have to be validated or reformatted before storage. Only when the data is actually in use is the structuring and, if necessary, formatting of the required data carried out. On the other hand, it contains structured data such as information in rows, columns or data records that have been sorted and processed with databases and data mining tools.

The water reservoir serves as a database in which the data is analysed. The water in the outlet contains the analysed data. Through this process, data can be sifted to derive useful business impulses marketing.

Data Warehouses vs. Data Lakes
If one speaks of the storage and provision of huge amounts of data, the term data warehouse is often used. However, the Data Lake and the Data Warehouse differ significantly in their concept and the type of data storage.

The Data Warehouse combines data from different sources and formats and structures the data to allow direct analysis. The data lake, on the other hand, takes data from different sources in raw format and stores it in an unstructured form in one place. The data brine does not need to know the subsequent type of analysis in order to store the data. When the data is actually needed, it is reformatted and structured.

The Data Warehouse focuses on key figures or transaction data. For example, images or audio files – the unstructured data – are not stored. Since the Data Lake provides the data in its original format, it can be used more flexibly. Accordingly, the data can be transferred into completely new structures and measured with new methods.

Creating competitive advantages
Due to the high amount of information provided, extremely meaningful and in-depth analyses are possible. This can result in important competitive advantages for companies. The exact analysis of sales transactions combined with customer opinions can decisively improve the price and offer policy.

Data Lakes: Fast & versatile
The saving process is very fast because the data is available in raw format. The ability to search through the large amounts of data is also very fast. The Data Lake offers more possibilities for the evaluation of the data than the Data Warehouse. However the Data Warehouse sorts out the information during storage that is not needed for later analysis.

Away from conventional data silos
Because Data Lake is able to incorporate scalability, agility, and flexibility, different types of data and analytical techniques can be combined to gain deeper insights. Traditional unconnected data silos and the data warehouse do not provide this feature. However, this feature is extremely important as the amount of data in the digital universe will increase tenfold by the end of 2020.

Data Lakes in practice
The main application areas of the data lakes are big data analyses. With the help of Data Lakes, the behaviour of customers can be predicted as accurately as possible in the future, since the connection between the reputation and purchase of the products under consideration and the purchase of similar products becomes apparent much faster. Thus, the customer can be better presented with suitable offers in the future. This is where the use of data lakes, artificial intelligence (AI) and algorithms comes in, because algorithms are able to include unstructured data in their evaluations.

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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.

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

DC: Dear Mr. Pahl, how important do you think predictive analytics applications are in B2B business?

MP: Anyone who wants to use their sales time more effectively has the advantage of data-driven support. For B2C customers, who in individual cases are looking for smaller amounts of money, I see the first leverage for predictive analytics in the segmentation area.
Predictive analytics is even more important in B2B business. The core element of data-based evaluations for sales is effective sales management by identifying suitable offers and sales opportunities. All areas can benefit from this. In the B2B business, for example, we have highly specialized key account managers in the company. It is extremely important to manage your time and focus it on the right activities.
When it comes to tailoring special services to key customers, we face a high-risk, high-return strategy. This means that a large customer can offer a promising opportunity, but at the same time there is also a high cluster risk. Predictive analytics applications can help here to approach customers with professional preparation and tailor-made solutions in order to increase success.
I would also like to consider the psychological component: Many field sales representatives only visit their existing customers because they experience a certain reluctance on the part of new customers. The phenomenon is similar to the classic call center employee in cold calling. At this point, matching with a predictive analytics tool is helpful to increase employee success under uncertainty. If the sales people are more successful with the new tools, the barrier to new customer acquisition disappears and growth targets can be better achieved at the employee level.

DC: What are the worthwhile benefits of predictive analytics applications?

MP: The applications from Dastani Consulting cover different areas. In my opinion, the focus is on „Next Best Offer“ for performance gaps, which are shown as so-called „White Space“.
I would like to look at the advantage from the sales side: Sales activities are becoming more and more complex and the service portfolio of providers in solution sales is becoming more and more confusing. At the same time, employees are confronted with more and more tasks and are working hard at their limits. For exactly this reason, the question arises as to how sales will be aligned in the future and how its tasks can be streamlined. Predictive analytics is able to uncover customer potential and propose recommendations for action with which sales can achieve success. The problem I experience myself is that many sales managers spend their valuable time administratively entering useless data into CRM and evaluating confusing data sheets. But these evaluations are never particularly good, because they were not made by a specialist. Exactly in this valuable time, in which the evaluations are carried out, the claim would rather be to say: How is it possible to send a sales manager with a precise forecast into the market?
Matching can take place in different dimensions and is incredibly diverse. As an example, I would like to mention a large training provider who can connect people who can work well together on a project basis within the framework of „Swarming“. Predictive analytics provides a solid basis for customer opportunities and the efficient use of resources – but it also helps to identify when the sales force is wasting time unnecessarily and creates space for more customer contacts.

DC: Do you think the applications are only interesting for large companies? You need a certain database to make predictions.

MP: An infinite amount of data is accumulated in B2C. In B2B, that would be much more worthwhile. However, less data is collected there, which in turn represents a major challenge for the database. The up-to-dateness of the data is also often low – the first step here is to do your homework and clean up your own data attic.
I also experience again and again that CEO’s want a toy robot because they have heard something about digitization that can solve any problem. Before you set up an application, you need a clean analysis of your processes. Predictive analytics applications are incredibly useful when the basics are right and they are connected to an effective point in the customer process. Mapping, in particular, of course encounters resistance, because every salesperson claims to be a great artist and that his processes cannot be grasped.
Digitization is therefore a possible option at the end of an optimization, but not an immediate goal. First of all, it is important to track down best practices and mistakes in the sales process to date and make the process measurable. Many companies fail to transform because they are introducing digital solutions without properly working out the analog processes. As a result, there is a lack of clear levers, starting points and namable goals in the end.
A competent consultant understands this problem. He knows when complex fields have to be decided by humans and when it is a matter of a linear process or pattern recognition that can be automated with a tool.

DC: What are the impulses of Dr. Parsis Dastani’s lecture?

MP: We received positive feedback about the presentation from many participants of the intensive seminar ‚B2B Marketing and Sales‘. Dr. Parsis Dastani showed us how important predictive analytics applications are in B2B business by means of many clear and realistic case studies. He clearly presented how the applications can be transferred to different industries. In addition, many participants were surprised that the applications can already be implemented with manageable means. In many industries, the question is no longer „if“ analysis tools should be used, but „when“ and „where“ – because the competition does not sleep.
In B2B business in particular, it is very important for managers to have someone at their side who can show them when digital solutions should be sought and when other optimisation options are available. Digitalization should be seen as an opportunity, but never as an end in itself. It is not a non-plus ultra, but an ad-on, if it helps.

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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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Application of Next Best Offer
Our developed Location Based App gives you and your employees Next Best Offer recommendations for sales in order to take advantage of the local proximity to your customers. This makes it possible for every tour that your sales representative plans to take full advantage of the potential. Nothing is more annoying than frustration and a futile effort to return from a distant tour without any prospect of successful sales growth.

Effective sales support
The developed AIMS (Artificial Intelligence in Marketing and Sales)-App is based – as the abbreviation describes – on artificial intelligence to provide the customer with the right offer.

The possibility of tapping attractive up- and cross-selling potentials exists, as the sales employee is already aware in advance of which offers the respective customer has an affinity for. On the one hand, the probability of customers buying will increase and on the other hand, sales will be enriched with profitable sales increases.

Quality through synergy effects
In the next tour planning, you and your sales staff can benefit from our low-threshold offer and actively approach customers with Next Best Offer. No additional equipment is required, except the familiar smartphone. There, the salespeople always have access to the sales proposals in the immediate vicinity and can be directed there directly via the integrated navigation system.

In addition to our predictive analytics forecasts, the AIMS Mobile app represents an intelligent further development, as it enables revenue-increasing synergy effects.

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Background
Imagine that: You are visiting an online shop with around 500,000 products. Behind it is a gigantic amount of data. This raises the question: How can we use this data to increase the conversion rate in the online shop?

The challenging task was to present the customer in the online shop with the goods he was really looking for. In the case of random product recommendations, it is quite unlikely that there is an item there that will ultimately convince the customer to buy.

Methodology
This is where our real-time forecasts based on AI help. They calculate which customer will buy which product next based on historical orders and purchases.
Our Next Best Offer methodology is used to predict the likelihood of your potential customers buying additional products from the available range – to which they have a high affinity.

The right content for marketing and sales: Individual recommendations
No matter whether Next Best Offer is used in personalised print mailings or in e-commerce – our company forecasts show that over 40% of the active customer base have purchased at least one individualised recommendation in the online shop. The larger the product range of an online shop, the more worthwhile it is to use Big Data methods. Customers want to be understood – this is exactly what our Next-Best-Offer makes possible.

In addition, we at Dastani Consulting have developed a mobile Next Best Offer app for the sales force, which provides sales information. You can read more about this in our next article.

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Der Beitrag Next Best Offer: With individualisation to more turnover erschien zuerst auf Dastani Consulting.

Meaning of the term „Next-Best-Offer
Companies are learning that using analysis and getting to know their customers on an individual level is the best way to significantly increase sales and customer loyalty. This means that retailers see greater success when they work with detailed and dynamic information about each individual customer rather than aggregating them as members of a broad segment at a specific time. The crucial point is direct interaction with the customer when it is relevant. What steps are necessary to transform new customers into loyal existing customers? The answer: General random offer recommendations must be replaced by individual personalized recommendations. This ultimately leads to customers buying so much more than planned in the online shop.

A few years ago, cost-intensive call centers were used in customer relationship management to tap cross-selling and up-selling potential. „Next Best Offer“ is a much more mature variant: if there is a risk that a customer should migrate, the focus is not on pulling a lot of money out of the customer’s pocket in the short term, but on keeping him there for the long term by addressing him correctly.

Predictive analytics methods are necessary to record the behavior, preferences, background and purchasing history of an individual customer. These include the black box, for example, purchase probabilities or article groups that are of particular interest to the consumer. This results in coordination at the right time with the right product for the individual customer. A continuous learning process is created with data-supported real-time analyses.

Advantages of the „Next Best Offer“
Even if the online shop comprises a five-digit number of products, „Next-Best-Offer“ automatically assigns each individual interested party the products they would most likely buy. The result: more satisfied customers. They feel valued because their wishes are recognized and they are addressed individually, separated from the broad customer segment.

Marketing specialists use the insights into the behavior of their customers (so-called customer insights) to promote sales, bind customers to the company in the long term and maximize customer lifetime value. By defining the next best step in the sales process for each individual customer, retailers exceed customer expectations and deliver the best possible customer experience.

Summary
The larger the online store’s product range, the more retailers benefit from Big Data analysis. As the product range increases, it becomes increasingly unlikely that a product will be included under the random offer recommendation that actually meets the individual customer’s requirements. For this reason, you can use the development of the Next Best Offer from Dastani Consulting to find the right offer for every customer. Even with limited resources, our analyses can help you to place individualized advertisements and to address customers in a decisive manner. The optimal presentation of the products leads to increased contribution margins in marketing and sales.

The choice of a customer-oriented approach with „Next Best Offer“ is the ideal way to significantly increase the conversion rate and turnover in e-commerce through optimal product presentation.

Companies that rely on machine learning and a data-driven business model are better positioned. They are able to take over relevant market shares from their countless competitors in online trading and thus specifically avoid the price war with other traders.

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