Sales provide revenue for the entire organization and sit at a critical midpoint in the customer journey. That positioning makes sales essential for growth and that process can be complemented by using data to make informed decisions. However, sales data can often be messy and incomplete which can make it frustrating when attempting to build predictive models. Understanding your data helps keep it organized and that data in-turn will help you build a better sales team.
Sales Data Roadblocks
When sales teams hit data roadblocks it usually falls into two groups: problems caused by a lack of data or problems using the data available. When sales teams lack data they tend to run on their gut intuition alone. Because of that, their roadblocks often include not believing in the power of data, not knowing what data is worth collecting, and/or not knowing where to start because everything they do is offline. Sales teams who do have data often experience problems with unstructured data, using form fields for CRM entries, and the time series nature of sales data. Although sales sit in a critical position in the customer journey, it is only one part of the overall customer experience. Involving other departments to create feedback loops provides necessary and better data to increase sales conversions which results in exponential gains for the entire organization.
Sales Data Uses and Use Cases
There are a few ways to define data and each requires a different approach so it’s useful to understand those definitions when attempting to utilize data. All data fits into 4 basic types once understood, you can use these data types for use cases such as lead qualification scoring, outreach cadence & messaging, customizing the pitch, predicting conversions, and nurturing lost deals. All of this allows you to optimize your messaging, customize your sales tactics, and streamline your team’s strategies to drive better conversions.
The first step to moving your teams to data-driven sales is to use automation to standardize the collection & imputation of your data into the CRM. Consider creating buckets of your information by what it impacts (such as features, pricing, messaging, and channel mix). Since sales take place over time, you have to start incorporating time into the analysis, including its seasonality. Unstructured data should also be incorporated, although it takes a special set of skills to utilize it offers a massive return on investment. To help that process, it may be useful to consider utilizing natural-language processing. As well as hunting down and destroying inefficiencies in interdepartmental collaboration and current frameworks. If you decide to replace frameworks look at using other high impact ones such as customer lifetime value, customer journey, and personas. Having that data knowledge helps you know your customers and drive your sales team to the next level.
Hungry for more actionable insights? Continue on to the next lecture Agile Customer Research For Product Managers.