- Dynamic guided selling requires clean, real-time data to deliver the desired results
- Three data management shifts will help organizations use AI effectively to improve performance
- Eliminate data cleansing as a daily task to maximize analytics capabilities
Recent studies have shown that data scientists currently spend most of their time cleaning and organizing data to ensure tools and systems can provide accurate information. However, this work effort slows the flow of data and prevents data scientists from building models and delivering critical insights to support business decision-making. A lack of accurate real-time data stunts an organization’s ability to implement dynamic guided selling. For those who haven’t read my previous blog posts on this concept (see “Supercharging Sales With Dynamic Guided Selling” and “AI Can Get You a Date, But Can It Keep Your Buyers From Swiping Left?”), dynamic guided selling is the automated collection of human and non-human seller activities and buyer interactions — and the application of analytics leveraging AI capabilities — to enable the delivery of near-real-time, context sensitive, role-specific guidance and coaching to sales professionals.
To implement dynamic guided selling successfully, organizations must make three data management shifts:
- Enable real-time analysis. Data cleansing should not be a daily job — it should be a project with the goal of fixing the issue and creating a structure that prevents future errors. Any ongoing data cleanup must be mitigated to enable the feedback loop required for AI, which is the engine that drives dynamic guided selling. For example, if an organization wants to use activity tracking to provide next best actions, reps can’t wait for the activities to be validated before receiving recommendations as the feedback will no longer be relevant. To work effectively, recommended actions must be presented in real time.
- Align on a customer definition. This may seem like a given, but many organizations haven’t agreed on how to define their customers. For example, marketing may define customers on the basis of the account-based marketing strategy, while sales may define customers on the basis of how territories are allocated. In this scenario, a large customer with multiple locations, departments, and contacts may get different messaging from marketing and sales due to a lack of alignment on the customer definition. Accurate real-time insights can’t be delivered without an aligned customer definition because the recommended actions aren’t based on a complete understanding of the customer.
- Eliminate the metrics debate. Accurate real-time data eliminates the need to decide what metrics to monitor. With clearly defined goals and agreed-upon criteria for success, results are based on whatever metric enables the best performance. Speculation on what to measure is not necessary in a world in which all interactions are visible and tracked. For example, if the AI system reveals a trend that selling a specific product yields higher success when bundled with another product, it can automatically adjust quoting to include that product as an option to enable sales reps to increase their win rate.
Although data cleanup is a large part of a data scientist’s job today, it must be eliminated before AI can realize its full potential. Is your data clean enough to implement dynamic guided selling effectively?