The establishment of machine learning and the resulting steadily increasing or more autonomous sales marketing automation are inevitable in the long term in most business areas. Lead management, which is particularly crucial for success in B2B sales, also benefits in many ways. Here in particular, significant competitive advantages in B2B sales can result from the generation and further processing of leads via artificial intelligence and, optimally, a considerable increase in qualified leads.
Today's ever-increasing web-centricity of private and business transactions enables companies to learn more and more about their own (potential) customers. Tracking users on the company website alone reveals a lot about specific goals, preferences and buying potential. Lead generation, in the course of which the company's own target group is motivated to actively provide central contact data of potential customers, as opposed to tracking, takes on a particularly important business role in terms of data collection, especially for B2B companies.
B2B buying processes are based less on emotional and more on rational considerations. Often it is a matter of investments with great consequences and, moreover, of products that have an increased need for explanation, which can hardly be fully covered by content. Precise personal contact with the customer is the key to success in sales.
In order to proceed as efficiently as possible in this regard, it is important to know one's own (potential) customers precisely at the base. Here, in particular, a qualitative data basis serves to establish corresponding personae. Already at this point, systems for pattern recognition in the use of online offers and predictive analytics, i.e. the use of historical information to predict actions, can contribute automatically to precisely defining the group of buyers. Deep Learning, the ability of systems to reason like the human brain, provides particularly accurate clues. As a result, it is possible to carry out very precisely coordinated sales measures for lead generation, for which the chance of receiving qualified leads is above average from the very beginning.
Of course, the work is far from done with the query of specific data. This data must be correctly interpreted in lead nurturing and used as effectively as possible for the further processing of the leads towards a purchase. This is done in the first step by playing out information that in turn must be precisely matched to the status and probable concerns of the leads. The goal is to convince the latter of one's own brand by conveying competence, proximity to the customer and, last but not least, by suggesting personal commitment. This process is usually very small-scale and involves enormous effort if carried out purely manually.
Machine Learning or Deep Learning and a corresponding Sales Marketing Automation contribute at best to the creation of a flexible adaptive solution, which not only leads to a significant employee relief, but also an increase in qualified leads and ultimately brings immense competitive advantages in B2B sales. Finally, the customer experience is significantly enhanced by an all-round economical (automated) system. Because (potential) customers always get exactly the attention they need or expect, which in turn increases the likelihood of achieving a strong long-term relationship with the customer.
In all of this, systems for collecting and evaluating large amounts of data, i.e. for pattern recognition, inference and classification, are of course particularly important. But chatbots and customer dialog with SalesBots can also make an important contribution to conveying the ideal customer benefits.
Generating leads via artificial intelligence, qualifying them and converting them into paying customers, or establishing systems that do this based on predictive analytics, machine learning and deep learning to create optimal sales marketing automation, is anything but simple. Before intelligent programs can get to work, real brains have to analyze and decide which components are required to achieve the respective goals. At the grassroots level, it's important to develop an AI strategy and roadmap, establish AI skills and knowledge, and start small and then scale as efficiently as possible.
First, those responsible must familiarize themselves with the basics and opportunities of artificial intelligence and automated processes. The aim is to create a holistic picture that incorporates benefits, goals, but also possible difficulties. Only with the right overview can ultimately proceed with maximum expediency. Thereupon, the fundamental question is clarified, i.e. how Machine Learning and Co. best fit into the business context. If lead generation is already being carried out, it must be clarified, among other things, whether existing processes can be expanded or whether a complete redesign is necessary. In most cases, this will result in various options that must then be mapped and prioritized in a roadmap. As the most important basis for an effective implementation of artificial intelligence in lead management, the right data must be available. Accordingly, both existing information and all data collection potentials should be recorded or analyzed as to how artificial intelligence (AI) can ultimately help to obtain information more efficiently. Likewise, KPIs should be defined that can be used to really determine the achievement of the goals.
Artificial intelligence requires, among other things, the creation of completely new skills and knowledge. The necessary resources must be created for this. To acquire the required AI knowledge, collaboration with an external partner is often unavoidable, but a competence team should also be planned internally. In addition to expertise, however, the right attitude and way of working are also important. Classic thought patterns must sometimes be completely discarded. Among other things, it is necessary to create trust in the technology and to consolidate the handling of it.
Once all this is done, lead generation begins with the smallest viable solution. Those responsible should be aware that not all goals can be achieved at once. In the first few months, the KPIs (key performance indicators) will quickly reveal successes and, if necessary, failures, which will be used to further develop the completed measures in sales. This shows when and in which direction further steps to determine relevant customer data through artificial intelligence make sense. In this way, all potentials will gradually emerge, which are to be integrated step by step into the overall concept. Setbacks and realignments are definitely part of the normal process here.
The generation of high-quality leads forms a significant key factor for long-term business success in B2B and B2C marketing.
- We develop and implement successful lead generation concepts to make the most of your potential and create the right conditions for efficient and personalised marketing measures.
- We support you in first defining and analysing your optimal target groups and then using relevant content to reach them exactly where they are digitally.
- Together with you, we develop a practicalnurturing process to generate qualitative leads along the entire customer journey.
If desired, we will be happy to implement amarketing automation solution that complies with data protection regulations - support in both the selection of the right software and the implementation and monitoring of your campaigns.