The digital transformation of the economy and society is progressing tirelessly. Companies are challenged to identify the potential for their own business in the new technologies to create growth opportunities. However, they should not just blindly follow trends. Artificial intelligence (AI) or machine learning (ML) in sales is not the end. Instead, those responsible should ask themselves: How can ML create real added value for people – i.e., customers and employees?
On The Open Sea Of Data
Nowadays, data in any form is created almost everywhere: Structured data such as, e.g., B. Data on click behavior, demographics or transactions, and unstructured data such as comments, likes, ratings, requests, photos, or videos. Filtering out continuously relevant information from this mass of data and using it for sales in a customer-centric manner exceeds human abilities. If you want to sail on course in this sea of data, you need the support of AI. And for AI systems, data is the wind in the sails: You need data to recognize patterns (topics, emotions, keywords, behavior), formulate forecasts (when will a customer buy? When does one jump off?), and create reports – and to learn from the past. This makes AI a powerful value-added tool for B2B sales.
Humans Can Perform Better With AI
ML can support sales reps to ensure a better customer experience and increase performance at every point in the customer journey. Sales teams can make processes more efficient and make the entire customer journey more tangible for the customer. Perfectly timed offers increase sales and increase customer satisfaction.
Is the man still necessary for this? Our unambiguous answer is: yes. Because it is the combination of the new technology, artificial and human intelligence, that offers real added value for customers. Picasso said: “Computers are useless. You can only give answers”. People still have to ask the right questions – and draw business-relevant conclusions from the answers. When sales teams leveraged ML, what would a classic B2B buying interaction look like? One approach:
Lead Generation And Qualification
During lead generation, ML can help create comprehensive interest profiles from structured and unstructured data. With so-called predictive lead scoring, the AI also determines which leads are most likely to be ready to buy. Sales staff can then analyze, interpret and classify the lists and the scoring in the business context. The data quality that AI delivers saves everyone involved tedious work and time – and increases the chances of success in converting the leads into deals.
Nurturing And Outreach
Lead nurturing (e.g., via content campaigns) increases interest – and initial discussions are held. Marketing and sales receive essential information about the needs of the customer side. In this phase, ML can be used for ad targeting, re-targeting, and displaying relevant content on your website: prospects are provided with personalized content on customer-relevant topics that the AI system has previously recorded, e.g., B. due to user behavior. Thus, on the one hand, the AI becomes a content curator, which provides each lead with tailored content: white papers, use cases, infographics, blog posts, or videos. On the other hand, AI-controlled chatbots can directly contact interested parties – which represents an enormous improvement in accessibility for sales and service. Bots can already guide customers and prospects to resources or answer inquiries. And if a request ever exceeds the machine’s capability, the chatbot forwards it to the human team, which takes over from now on.
Presentation And Introduction
When it comes to presenting the product or service, the provider side can use AI-supported prototyping, among other things. Thus, for example, digital twins of products, machines, or buildings can be created, which the customer can look at – and adapt to their own needs when making a purchase decision. This is where service and sales merge. What is even more explicit: the product can be experienced. The B2B customer experience increases. The sales managers can also answer questions and ask the right questions to signal to the customer that they have come to the right provider.
Negotiations And Closing
Today’s customers are better informed than ever. When they enter into negotiations with a provider, they have already obtained information from other competitors beforehand. AI systems can collect competitive information and process it for the supplier side. This means that those responsible for sales know what the competition offers and can, e.g., B. Find arguments on sales battle cards to emphasize your offer advantages. This increases the chances of a positive purchase.
Follow Up And Upsell
ML helps companies create comprehensive customer profiles from structured and unstructured data to identify new needs before the customer even thinks about them themselves. At the same time, AI-controlled chatbots can be used in the service. The service and sales teams can address newly identified customer needs for upselling and cross-selling. With the help of predictive maintenance, products (such as machines or systems) can also be serviced at an early stage. This minimizes downtime and increases customer value.
Added Value Machine Learning
The market research company Gartner writes in its report “Future of Sales” that by 2025 80 percent of interactions between providers and customers will take place online. ML can be used in various locations and is particularly effective when viewed as a complement to the human – not as a substitute. Because: ML must be customer-oriented, and people must come to the fore in the right places. Customers need less direct contact with salespeople, but they do need it. Anyone who can anticipate customer needs through ML creates immediate added value and increases customer satisfaction.
Employees Need Data Qualification
That is why it is also necessary for those responsible to qualify their sales and service teams accordingly. Today, employees should know how to place the high-quality data benefits identified by the AI system in a business context and use them to create relevant information and attractive offers. Anyone who invests in their service and sales teams’ targeted qualification creates a powerful combination of man and machine – a high-performance winning team that ensures future viability. In our high-tech world, companies’ task is to make the customer experience more human again. When AI provides prospects in the customer journey with hyper-customized information and service and sales teams with high-quality data,