Predictive Sales analytics: Strategizing sales workforce
Being a fast-growing digital economy, the data on customer analytics, business intelligence and buyer interest is on a rise in the market.
Traditional business analytics through analyzing existing customer persona, is behind the times and slows down sales cycle: What the market demands now is the intelligent use of predictive tools!
Predictive sales analytics is one such predictive analytics tool which forecasts and anticipates the sales economy or the prospect’s behaviour through historical data powered by machine learning modules and algorithms.
Predictive sales insights aid organizations to design tailor-made campaigns for buyer personas, identify hidden patterns of opportunities, formulate the sales narrative and many other derivatives enabling agile prospect planning and quick deal closure.
The primary factors considered for sales forecasting are the historical trends, including the cyclic events, long-term sales trends along with their causal factors like the customer’s industry. With the power of big data and new-age technologies like Machine learning, the primary factors are integrated into the model to predict future trends.
The analytics models are ever-evolving based on the changing trends, real-time data feeds, transactional data, etc. Retail giants like Walmart and Amazon are using predictive sales tools to generate personalized product recommendations for the customers and for stocking the shelves according to previous buyer choices.
Even though there is so much buzz about predictive sales analytics, a study by EY and Forbes insights reported that many key decision-makers hadn’t maximized their derived data insights due to the lack of predictive analytic tools.
Companies are rapidly adopting predictive tools to reduce the risk and engage in smarter prospect planning with accurate sales forecasts. A data analytics leader’s study reported gains in operating margins and revenues up to 15% or more after adopting predictive tools.
Predictive sales tools: Its use in industry
Major use-cases for predictive sales tools are as follows:
1) Predictive lead scoring: Predictive lead scoring identifies customer journeys, uses these trends to predict the likeliness of a prospect to close the deals. It assists the sales team in quantifying and prioritizing the prospect opportunities.
2)Predictive forecasting: Predictive forecasting uses big data to predict the expected revenue and prospect opportunities based on current and historical sales data. It helps sales teams with accurate forecasting insights on sales performance.
3)Ideal customer profile (ICP): Predictive sales analytics can help in creating an ICP for different products or marketing campaigns, thereby strengthening the sales and marketing initiatives of organizations.
Predictive sales analytics tools serve a huge value in the sales and marketing initiatives of the companies through predicting the propensity of customer personas to adopt a product, customer attrition risks, etc.
With more customized insights on customers, sales and marketing teams can design personalized campaigns and target the prospects efficiently. It also assists account managers to take proactive measures to avoid customer attritions.
Tech giants like Facebook, Google and Microsoft are constantly competing in the industry with new age predictive tools to empower sales teams which in turn indicates the significance of predictive insights in the arena.
Draup for Sales platform is a sales enablement tool that aids users with sales forecasting insights through deep- dives ranging from the company’s outsourcing probability, tool adoption propensity to digital projects.
Draup has developed internal indices like opportunity index to predict and quantify the high probable opportunities for sales and marketing teams thereby reducing the efforts on low probable leads.