Businesses today can relatively easily gather and generate a lot of data about their customers, operations, and performance. However, abundant information from CRM systems, ERP platforms, and marketing campaigns does not directly lead to better sales figures and higher profits.
Data science is the catalyst that turns raw data collected from multiple sources into actionable insights that can improve the bottom line. With access to better data-backed intelligence, companies can adapt their business strategies to capture the most value in their market. By improving the quality of information and business decisions stemming from it, data science enables inbound and outbound growth.
Besides evaluating past performance, data science is a powerful tool for creating predictive models that can highlight changes in customer behavior, market trends, and potential opportunities. Machine Learning (ML) and Artificial Intelligence (AI) powered systems can monitor and process incoming data in real-time to anticipate outcomes based on historical patterns.
72 percent of the fastest-growing B2Bs say their analytics are effective in helping them with sales planning, compared to 50 percent of the slowest-growers. Source: Mckinsey
Inbound growth from data-backed insights
Data science tools are instrumental in improving interactions with existing customers. By evaluating previous sales, predictive analytics can help trigger responses and suggest products to guide clients on a personalized purchasing journey. Specific steps can be programmed to automate lead nurturing and qualification that save time and create new sources of revenue. Behavioral insights can be used to determine what products and services customers are more likely to be interested in.
Predictive metrics can be used to monitor and identify customers who are almost ready to purchase, giving sales departments a head start. With more information to work with, sales teams can adapt their approaches and sales pitches to maximize chances of successful conversions. Data science can crunch information about client companies, their size, budget, and needs to create the best possible offers.
Willingness to pay is an important metric that is vital for setting the optimal price for a product or service based on customers’ needs, motivations, and preferences. Data science analytics take the guesswork out of the equation by identifying price tiers and how they can be adapted to improve profitability. This data can be used for A/B experiments learning to determine the optimal prices or fees for consumers.
Offering existing clients more well-priced products and services not only helps to take advantage of recurring revenue but to improve customer satisfaction and retention. By using customer behavior as an indicator, data science tools enable companies to identify customers who are ready to buy more and those who are close to switching to a competitor. In both cases, businesses can capture more value having identified them. In the end, retaining customers is a lot cheaper than acquiring new ones.
Source: McKinsey
Data-driven outbound growth
When combined with data science, sales prospecting generates higher quality leads as the process incorporates multiple data variables in the search. By identifying points in customer characteristics that are more likely to result in a sale, data science tools can generate well-defined and comprehensive buyer personas. Better targeting improves the effectiveness of marketing and advertising efforts as they can be aimed at the right segments at the right times.
With access to data science insights, sales and marketing teams can vastly improve their decision-making, structure, and forecasting. Better data means sales quotas can be much more aggressive while remaining precise and achievable. Incorporating external factors, seasonality, new products, and other metrics helps teams see the bigger picture and forecast sales demands accordingly.
Data science enables companies to align their sales expertise and resources with their covered territories to deliver optimal results. Predictive analytics can point to new selling models, sales policies, and revenue distribution among salespeople. Operations-wise, data science tools can point to more intensive quarters which require additional resources to handle the future increase in demand. By preparing ahead of time, companies don’t miss out on valuable opportunities.
Data science is a powerful tool that can have a significant positive impact on sales and revenue by driving inbound and outbound growth. Predictive analytics can transform sales, improve operations, and extract the full potential from gathered information. Deep insights generated from business data provide greater accuracy and more control in business decisions that steer future performance.
With a strong data science toolkit, companies can collect, organize, and analyze their data to attract, convert, and close more customers.
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