Ever bought a product or a vacation, and it seemed to suddenly pop up on your search page or in your email inbox? If you said yes, then that’s an ML algorithm at work, monitoring your online activity and making relevant recommendations. With AI becoming an everyday reality, the role of sales teams is constantly changing. A Harvard Business Review study found that sales teams that adopted AI saw more than 50% increase in leads and appointments, cost reductions of 40%–60%, and call time reductions of 60%–70%. Intelligent ML driven sales technologies are streamlining selling by reducing time-consuming manual tasks and increasing productivity. They are also automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting and reporting. Here are the top four areas where ML algorithms can be leveraged to help your sales team sell more and drive business growth:
ML and Deep Learning models allow sales teams to incorporate different types of data, structured or unstructured, and combine those data sets into the forecasting model. This approach incorporates all historical selling, pricing and buying data that could impact future sales in a single ML model, which improves the accuracy and scale of sales forecasts. In addition to this, these models also behave like a long-term memory function and are better at learning patterns over time, especially when historical data is incomplete or non-existent, like forecasting new products and services. ML enabled tools excel by quickly iterating to fill in these gaps. They provide accurate forecasting in these areas, thereby reducing the risk of investing in entirely new selling strategies for new products and services.
Using Predictive Lead Scoring, your sales teams can score their leads by how well they fit into your company’s successful conversion patterns. Using a combination of data science and ML, Predictive Lead Scoring helps you to discover the patterns of lead conversion in your business and predict which leads to prioritize. By using ML, Predictive Lead Scoring provides a simpler, faster and more accurate solution than traditional rules-based lead-scoring approaches. ML and AL based tools enrich the insights derived from third-party data, the prospect’s activity at events and on the website and from previous conversations with salespeople and rank the opportunities/leads in the pipeline according to their potential of closing successfully. They also help turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), thus strengthening the sales pipeline in the process.
ML powered models excel at pattern detection, which means they can easily match data profiles with their most valuable customers and identify new prospects with the highest potential to convert. By using a series of attributes, characteristics and their specific values, sales teams can identify potential prospects in a fraction of the time it took earlier, thereby saving them thousands of manpower hours in a year. Apart from new prospects, ML driven models can also be used to identify upsell/cross-sell opportunities within existing accounts, thereby increasing sales revenue and bringing down overall marketing costs.
Improving Sales Team’s Productivity
Sales managers need to assess their pipeline status, targets and their team’s performance on a monthly basis. Often in sales teams, the performance levels vary widely. Sales managers can bridge this gap of productivity by identifying the top performers and applying their best practices across sales teams. ML driven models can identify which actions and behaviors correlated with the highest close rates and sales managers can then use these insights to scale their sales teams to higher performance. Further, these insights can be selectively used while recruiting new candidates into the sales team. Those that have comparable capabilities with that of their top performers are likely to be the best fit for any sales organization.
With large user databases, manual analyzing is nearly impossible and consumes too much time. Machine Intelligence is therefore the need of the hour, and can accelerate sales, drive results and save marketers’ time.