Smart Data Sales Automation

How can you successfully hold your own against the competition in dynamic predatory markets with ever more comparable services, prices, and service offerings? If the provider and the service are interchangeable, there are two main differentiators - first, the human factor contributes significantly to sales success. Despite advancing digitalization, 67 percent of respondents in a study of more than 1,000 B2B (business-to business) companies said that the salesperson is one of the main criteria in the purchase decision. On the other hand, however, a salesperson should also know with which customers and in which regions there is the highest probability of closing a deal. Thus, one of the most influential competitive advantages lies in innovative and intelligent data analysis, as well as its targeted use in traditional business-tobusiness(B2B) sales.

In the age of Big Data, most companies have countless amounts of data and customer data. Only a few companies are able to profitably analyze or use this data to gain a holistic picture of their customers, to expand sales with existing customers, or to acquire new customers. In order to face these challenges and to develop a sustainable competitive advantage, we have been working with our partner Porsche Consulting on the topic of Artificial Intelligence (AI) and the use of Smart Data in B2B sales.

With the goal of increasing the utilization rate with our customers in order to generate sustainable turnover and growth, we developed an innovative model to control sales in an automated and potential oriented way. Through smart sales management and targeted market development, we also wanted to relieve the sales force of administrative tasks in order to increase the number of on-site customer visits.

In order to activate the market exploitation by our sales team, we created a network target picture for defined regions and services based on a three-stage implementation plan, on the basis of which we derived a sales strategy, and launched a pilot.

In the first stage, we created a potential map for our target regions. To accomplish this, we first established hypotheses and parameters for piloting through interviews. Subsequently, we defined important key success factors (KPI's) for the data model from a customer and capacity perspective to obtain a 360° view.

With the goal of meaningfully optimizing the internal data quality through selected external data sets, we used data crawling to search the Internet for our defined KPIs in the second stage. Data crawling works with so-called search bots, which are programs that automatically and repeatedly perform predefined tasks. With these data sets we refined our data and reclassified customers. For this purpose,we analyzed, for example, selected websites, visual sources, as well as people and mobility data.

With the refined data model, we sharpened our customer profiles, created a potential map, and generated concrete recommendations for the sales organization. In addition to classic segmentation by customer group, we also used socio-economic clusters (regional purchasing power) and identified a sales potential for each prospective customer.

To ensure scalability, we defined the recommended actions in a customeroriented sales process and integrated the data model into our customer relationship management system (CRM) as a target customer list.

In the third stage, we started piloting via an iterative process. In this stage, sales activities were aligned with the network target picture based on an implementation plan. The digital data model was validated through customer visits. Due to automated lead generation and qualification, we not only increase the speed and efficiency of our sales organization, but also continuously optimize our data model by collecting smart data.

By using AI in our CRM system, we also receive valuable recommendations for action from customer interactions and can thus improve opportunity and customer management, as well as derive logical next steps and further recommendations for action.

Fundamentally, the incorporation of Smart Data and the incorporation of intelligent algorithms plays an increasingly important role and has the potential to bring about great improvements. It is becoming a critical success factor for companies to embrace this revolutionary technology, not only in sales, in order to remain competitive and innovative.

Our goal is to permanently manage our sales organization more efficiently through automation in order to act more proactively on customer requests. However, when it comes to implementation and closing, the human component often remains the decisive sales factor.