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LEAD Innovation Blog

Read our latest articles on innovation management and innovation in a wide range of industries.

Date: 03-May-2019
Posted by: Angela HENGSBERGER

Artificial intelligence: potentials and application in B2B sales

 

Artificial intelligence offers promising opportunities to increase sales effectiveness. AI systems take over administrative and repetitive tasks, equip sales staff with important information and are a valuable decision resource for sales control. Read about where artificial intelligence is most commonly used in B2B sales and the benefits it brings.

 

Competitive advantages through artificial intelligence

Artificial intelligence (AI) is much more than just hype. Sales can achieve sustainable results and competitive advantages with Advanced Analytics, Predictive Planning and Machine Learning. A study by IT service provider Tata Consultancy Services shows that by 2020, IT, customer service and sales in particular will benefit from artificial intelligence - especially in Europe and North America.

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Graph: Business areas that will achieve the greatest competitive advantage through AI by 2020, Tata Consultancy Services: How AI is elevating the performance of global companies

 

Paper Innovation marketing

 

B2B sales are mostly positive about KI

A recent survey by the software company Qymatix Solutions GmbH was devoted to the question of how decision makers in B2B sales evaluate the use of artificial intelligence in sales. The survey was conducted by around 70 managing directors and department heads of medium-sized companies in wholesale, medical technology, information technology, and other sectors from Germany, Austria and Switzerland.

The main results can be summarized as follows:

AI systems are currently not very widespread in companies, although there is interest. Only 16 percent of companies already use predictive analytics or plan to introduce AI-based forecasts in sales. 63 percent of those surveyed were interested in the topic, but had not yet had time to deal with it. 18 percent were not interested in AI in sales because they did not see any concrete application possibilities in their company.

With regard to the expected benefits, respondents see the greatest economic benefit as being in:

  • an increase in the efficiency of sales activities and customer development, including cross-selling and up-selling (82%).
  • Sales planning and controlling (70 %)
  • Reduction of migration risk (63%)

In the eyes of managers, however, artificial intelligence is not without challenges. One of the most frequently mentioned obstacles is

  • Data quality (45 %)
  • Lack of understanding and transparency of CI calculations (40 %)
  • Price (71 %)
  • Integration of AI in ERP and CRM systems (65 %)

The fundamentally positive assessment of AI systems in sales is also reflected in a study by the McKinsey Global Institute. According to this study, 88 percent of those surveyed assume that artificial intelligence will make their work easier within the next 10 years and see the development as progress. Only around 6 percent fear that the technology will make their job more complicated or take over completely. This attitude permeates all positions - from junior to executive.

 

Possible applications for AI in sales and distribution

Companies that use AI sensibly in sales and create a common database in cooperation with marketing benefit from valuable information about their customers. Since many AI processes can be automated, they relieve the sales team of time after the initial phase. The time saved can in turn flow into the actual sales work.

 

1. Dynamic Pricing

Dynamic pricing is not based on costs, but on price acceptance by customers as well as supply and demand on the market.

The flexible adjustment of prices on the basis of market demand is nothing new. Online players such as Amazon, however, present traditional traders with new challenges as they can automatically change their prices in near real time using algorithms. An intelligent algorithm sets the price for individual customers in such a way that they are ready to buy, but at the same time sales do not suffer. In addition to demographic characteristics, AI price optimization also uses results from the analysis of customer behavior as a data basis: 

  • Prices that the customer has accepted in the past
  • Behaviour of similar customers
  • Current price developments on the market
  • Other factors relevant to successful transactions in the past include

The advantages of dynamic pricing thus lie in the automatic implementation of price changes in the event of changes in the market environment, the adjustment to the actual willingness of customers to pay, and greater efficiency.

dynamic prices

Source: Dynamic Pricing Optimization, Firebear Studio

 

Dynamic pricing is currently used primarily in the online sector. As many medium-sized companies already have an online shop, this optimized pricing is no longer just an option for the big players. It can also be used sensibly by smaller companies. A prerequisite for this, however, is a high level of data accuracy, availability and completeness as well as the preparation of the entire company for dynamic pricing (e.g. adaptation of printed price lists, marketing measures, etc.).

 

2. Predictive Lead Scoring

Predictive Lead Scoring uses forecast-machine-learning algorithms to analyze existing customers to determine how likely it is that a lead (= contact) can be won as a customer.

With artificial intelligence, the sales force gains in-depth knowledge about the customer and increases the probability of a successful conclusion, as it can concentrate on promising customers and address them in a targeted manner. The AI application evaluates which behavior and which characteristics make an interesting lead for sales (= Sales Qualified Lead). On the basis of this data, those leads can then be identified that are ready for a sales talk and can be forwarded to the sales department. The remaining leads have to be further supported by the marketing department. Data from third parties can also be included in the analysis to provide sales staff with qualified, promising leads.

 

Best Practice: Lead Scoring Strategy from Harley Davidson

One example of an impressive track record is Harley-Davidson in New York, where the use of the KI Albert has resulted in a 2.930 percent increase in sales leads. The technology focuses on behaviors that lead potential customers to contact Harley Davidson. For example, the AI found that advertisements with the "Buy!" prompt generated significantly fewer responses than those with the "Call! By exchanging a single word, the number of responses to ads placed increased by 447 percent over the period covered.

Another successful method was the determination of high-value past customers. The AI selected those individuals who had either already purchased a Harley-Davidson product or added it to their online shopping cart, or were among the 25 percent of website visitors who had spent the longest time there. These "high-value past customers" were used as a basis to identify "lookalikes" that were not Harley-Davidson customers, but otherwise met many of the group's criteria and were therefore excellent leads.

Harley Davidson

Picture: Harley Davidson uses online sweepstakes to generate data that can be reused in AI systems.

 

Predictive Lead Scoring thus makes the evaluation of sales opportunities not only more effective and scalable, but also more objective - i.e. independent of subjective factors. Systems of this kind are usually already integrated in marketing automation systems such as Hubspot. With this AI tool, a company is able to sort out less promising contacts from the outset and thus relieve sales.

 

3. Forecasting

Products and services sell best when demand is particularly high. When exactly this is the case can be tracked with AI from data.

Forecasting can help predict potential sales results based on data-driven probability models. Artificial intelligence and predictive analytics increase the quality of a sales forecast and revenue forecasts. Business decisions can be better managed, goals more clearly defined and budgets and resources more accurately determined. Good forecasting models also adjust forecasts in the meantime or provide early warning signals to avoid excessive deviations from targets.

 

4. Cross- and Upselling

Algorithms can greatly improve the basis for selling an additional product or service to an existing customer.

With the help of artificial intelligence, detailed shopping basket analyses based on CRM and ERP sales data can be created prior to cross-selling in order to calculate and predict the probability of successful cross-selling. Sales managers are provided with a sound basis for deciding when it is worthwhile to offer a customer an additional product or an upselling offer. AI platforms such as Jetlore, for example, analyze and interpret hundreds of online store pages to understand consumer preferences. The underlying AI uses the customer data to create rankings of which customers might be particularly interested in certain products or processes. In addition to a variety of other functions, the tool enables effective acquisition and delivers statements as to which leads are also suitable for future projects.

 

5. Customer satisfaction

Self-learning AI systems can improve customer experiences and thus customer satisfaction on the basis of existing data and learn with each new data record.

AI can be used in many different ways in customer service. In most cases, AI solutions support the account manager, e.g:

  • Automated interaction with the customer in the form of shopping assistants that support the customer in finding the desired product.
  • Chatbots who take care of customer complaints
  • Personalized customer approach with the help of AI systems
  • Detection of fraud using AI solutions
  • Faster response and processing of customer inquiries through supporting AI systems
  • Manage customer experiences from an Omnichannel Perspective

According to a study by the Capgemini Digital Transformation Institute, 75 percent of companies using AI and machine learning have increased customer satisfaction by more than 10 percent. Conversely, this also means that fewer customers can migrate and new customers can be won. The use of AI technology in customer service therefore also increases sales and turnover.

 

Conclusion: Artificial intelligence in B2B sales

The successful introduction of artificial intelligence in sales will bring a significant competitive advantage in the coming years. With the help of AI algorithms, sales has the opportunity to deepen its customer knowledge and increase the probability of closing a deal, as it can concentrate on promising customers and address them in a targeted and individual manner. Please also read our article on this topic:

How digitization is fundamentally changing business models of industries“.

10 Reasons for Innovation Marketing

Angela HENGSBERGER

Born and raised in Vienna. Since 2012 she has been in charge of Business Development at LEAD Innovation with the functions marketing, sales and communication.

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