How To Develop A Data-Driven Sales Approach
As customers' behaviors constantly change and expectations increase, sales representatives must rely upon technology and data to make sales-related decisions to drive revenues.
A 2020 LinkedIn survey revealed that 56 percent of sales representatives use data to locate their target customers. The number of people using sales intelligence tools has increased significantly by 54 percent since 2018. The COVID19 pandemic has turned sales in all industries upside down and accelerated the shift toward a data-driven sales approach.
The importance of data analytics
Buyers now have the power more than ever; they expect brands and sales reps to understand what they want and to meet their expectations. When talking to a sales rep, customers expect a solution that can solve their problem rather than a quick purchase conversation. These expectations can be solved by data analytics.
By understanding what customers want, based on data, not guessing, businesses can provide a more personalized, targeted, and relevant sales experience to their customers. A data-driven sales approach means that the sales team collects and uses data to make sales-related decisions, from the products to the time they should reach out to their customers.
Let’s have a look at how data analytics can drive revenue:
Stronger value proposition and better pricing
Organizations can understand the value that different types of customers derive from their solutions better via collecting and cross-referencing multiple data points. In this way, organizations can set a stronger value proposition for their product and structure their offerings. Sales reps must learn to determine prices and strike a balance between profit and customers' affordability. Therefore, this requires collecting and analyzing data points related to the industry, competitive landscape, and consumer spending patterns.
Improved segmentation
To effectively segment buyers, companies need to have a solid understanding of what their buyers want. This segmentation allows businesses to prioritize high-value customers and those who have the propensity to buy based on their historical data and their future purchase intentions.
A more effective way to convey messages
With the world moving to 100 percent digital, we are exposed to around 4,000 to 10,000 ads per day. A consistent and effective sales content and messaging become incredibly critical as it helps brands to stand out. Sales teams can better understand the types of content assets that work best to engage customers and drive sales by using content analytics at every stage of the customer journey.
Improved consistency with customer journey
The sales process has become more complex, non-linear, and distributed across multiple channels in recent years. Customers expect brands to provide them with a seamless, personalized, and targeted experience across all channels and throughout the customer journey. Delivering this type of service personalized and relevant to each stage of the customer journey requires a panoramic view of the customers and powerful analytics to improve decision-making.
Why is the data-driven approach necessary?
COVID19 came along with many changes, and one of them is the rapid acceleration of digital lifestyles and the way business operates. Due to the pandemic, 80 percent of customers expect to do more online shopping than before. The balance between online and offline interactions drastically changed in 2020.
The pandemic has brought many shifts in buyers’ behaviors, needs, and budgets. Consequently, businesses can’t rely on historical purchasing patterns anymore. Companies need to understand the changing market conditions and customer patterns via real-time data-driven.
Best practices for developing sales methods based on data
A data-driven sales approach cannot be achieved by investing in new technologies solely. Companies must optimize their data and business processes to provide actionable insights that are converted into business outcomes. Here are some practices that companies can take to develop a data-driven sales approach.
Adjust insights according to the needs of the sales team
When sales representatives find data too complex and opaque to adapt to their sales process, they prefer to go with their experience and expertise; this is why data analytics will fail.
To overcome this resistance, businesses should pay attention to adjusting analytics to fit the needs and challenges of the sales team and provide them with customized dashboards and decision-making tools.
Adjust objectives, metrics, and sales data
Data collecting is not a difficult task for many companies, but the conversion of data into actionable insights is. To avoid data overwhelming, companies should set specific business goals, identify the correct set of metrics to accomplish those goals, and then construct an analysis consistent with those.
Provide the skills and knowledge needed to adapt to data-driven sales
For the sales team, the shift from trusting their experience and “intuition” to data requires a lot of rethinking of the sales process. Therefore, training is needed for them to understand and apply the insights gained from the analytics. Such activity will also help sales representatives overcome their sense of intuition and expertise, leading to better results than real-time data.
Consider using AI and machine learning for data analytics
When reliable data is combined with AI and machine learning, sales reps can gain more accurate insights on the most relevant potential customers and prioritize their sales efforts. AI and ML can also be used to guide sales, provide information about the right content and the most relevant messages at different stages of the sales conversation.
Break down silos to fully understand the customer
A comprehensive understanding of customers requires companies to integrate data from all customer touchpoints. However, 54 percent of buyers often feel that sales, service, and marketing do not share enough of the information they need. To solve this problem, data of all customers across all customer-facing teams in the organization needs to be merged.
Conclusion
When all buyers’ expectations continue rising, integrating data and data analytics into the sales process can significantly eliminate the risk when guessing customers’ behaviors. However, you have to restructure your sales processes to effectively leverage the use of analytics to be more responsive to the patterns uncovered by data.
