Using data mining to define your optimal sales strategy
Using data mining to define your optimal sales
strategy
Collecting and evaluating data in a targeted manner and using the results quickly for operational purposes: In more and more industries, these skills are decisive for success. All business areas are affected by this, but sales in particular. However, there is one challenge: in no other area is personal communication as central as in sales. In the end, especially in B2B sales, no deal is closed without interested parties and suppliers having a direct exchange. Successful sales organizations approach target customers more quickly and consistently use these conversations to identify their needs. This success factor can be specifically optimized by analysing call data. In this way, you can use data mining to continuously refine the definition of a successful sales strategy - and thus provide the entire team with relevant and quickly actionable insights for day-to-day sales work.
Data Mining Definition
Digging or mining for information: The term data mining vividly illustrates what this discipline at the intersection of IT and statistics is all about. The experts use large volumes of digital data to identify characteristic patterns and test corresponding hypotheses. If these correlations are confirmed in the data analysis, they can be used productively in business. Example: Which pitch statistically offers the best chances of success? Data mining creates models that answer this question based on the data known about the person of interest. In the background is the knowledge from all previous conversations.
Sales is digital, but digital enough?
"We've been digitally positioned for a long time!" That's what many decision-makers think when it comes to using digital potential to acquire new customers. And indeed, many sales organizations have already taken the first step. They have firmly anchored the use of their CRM system in their processes. As a single source of truth, it records all points of contact with existing customers and interested parties.
That is the indispensable basis! However, sales teams can achieve much more in the next step, namely in the quality and usability of the information obtained. Currently, salespeople cannot find any more information in the CRM system about the last touchpoint with a prospect than the appointment, contact person and next action. This is far too little to use data mining to define the next steps. To take the next step towards Sales 4.0, you need information that will help you address potentially interested parties more quickly and better understand their individual needs.
What nuances were recognizable in the last conversation? What was the reaction to the pitch and what objections did the other party raise? These are all indications of the prospect's important needs that salespeople need to address in further contact. Only when this valuable information can also be recorded can the potential of digital processes be utilized. This is because the conversation information then becomes the basis for the targeted planning of the subsequent sales process - with automatic support, as the white paper Arts meets Science in Sales shows.
Data mining uncovers conversation patterns
According to the general definition, data mining also reveals patterns in conversations that would remain hidden to sales staff without digital support. Up to now, every sales person has primarily worked according to the maxims that their own experience and gut feeling dictate. What is missing are objective guidelines that provide more certainty. Which successful strategies do other team members perhaps use unconsciously? What can I adopt for my own daily work? This comparison is difficult because there are no records of interview behavior and no automatic evaluation routines - a real hurdle for better sales onboarding.
However, this perspective can be integrated into the existing CRM system with the help of additional software. This transforms the sales conversation from a black box into a transparent process that can also be analyzed retrospectively: Conversation Intelligence.
Data mining at bao
bao uses data mining to bring its users even closer to their own target group. Better conversations as the key to more sales success - this is how the Conversation Intelligence solution makes it possible: bao forms the basis for data collection in the form of digital conversation guidelines. Based on a defined structure, the digital conversation guidelines guide you through the appointment. Digressions are of course possible, and the interactive guidelines then reliably steer the conversation back to the structure. During the conversation, the guide ensures that the salespeople ask all the must-have questions and always have important product details top-of-mind. The other person's responses can be recorded by mouse click and keyboard input. At the end of the call, a structured call log is entered into the CRM system: the ideal starting point for preparing the follow-up appointment. So no more scrolling back and forth, no more lost notes! This also facilitates collaboration within the team, for example when SDRs hand over the lead to account executives.
This is the view of an individual opportunity. It is expanded to include the overarching view. Which pitch performs better in the A/B test? What are the top objections that everyone in the team should be able to effectively refute? Or even better, how can these objections be anticipated with a better pitch? bao's dashboards are fed by the digital meeting minutes. In this way, every single sales conversation contributes to improving the approach to prospects based on data. In this way, bao offers a systematic approach to increasing efficiency in sales through better communication. The definition of data mining in sales therefore also has a strategic component.
Using data mining to define the sales strategy
Sales needs more data-driven support in order to implement its strategic goals in a targeted manner. With data mining, sales teams can work out their definition of the target group better and better and derive the right recommendations for action. This brings acquisition into the digital age.
How are customer wishes and customer behavior developing? What customer lifetime value does a particular contact represent? These are just a few examples of key questions in sales that data mining can answer. Based on this, concrete next steps can be derived in the sales process. bao starts at the point where success or failure in the sales process is decided: the conversation. Start building up a database now in order to consistently improve the way you conduct conversations, refine your strategic orientation and open up many more evaluation options for the future.
