Improving Sales with Data Analytics: Expert Reveals 6 Keys to Know

Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, OxyLabs VP of Enterprise Sales Andrius Palionis offers some expert advice on improving sales with data analytics and advice for doing so yourself.

Many sales executives know the benefits of relying on customer insights when making strategic business decisions. Most organizations have an abundance of information at their fingertips – data can help in many ways, from making strategic decisions to improving the bottom line.

It may be as simple as the number of calls made to a particular account by a salesperson or as complex as analyzing transaction records across entire product lines to uncover hidden patterns and develop a better understanding of trends and opportunities.

Many executives, however, are not taking advantage of such data. They lack a clear understanding of where to start when analyzing internal and external information.

It is particularly true for sales teams that are traditionally lacking data analytics expertise. Historically, only IT professionals used business intelligence (BI) software. However, BI technologies have progressed to become more user-friendly and intuitive, allowing for widespread use across a broad range of organizational domains.

Improving Sales with Data Analytics

Data Analytics to Increase Sales Productivity

Before we go into sales efficiency, let’s define BI and AI. Although sometimes business and artificial intelligence are used interchangeably, a more precise way of thinking would be to treat BI and AI as independent yet complementary technologies.

“Intelligence” in AI refers to computer intelligence, while the same term in BI means intelligent business decision-making that data analysis may provide. BI can assist businesses in organizing and acting upon the vast volumes of data they acquire.

Nonetheless, these solutions are insufficient to aid managers in providing the necessary reports for data-driven decision-making. As a result, one solution to this challenge is to integrate a Business Intelligence (BI) tool that enables better decision-making.

While there are several methods for increasing sales productivity, integrating data from other departments and APIs to improve lead information is highly underused. Incorporating data enrichment and analysis into sales operations can help companies reach fantastic results.

According to a Harvard Business Review study, existing BI technology can automate 40 percent of the time spent on sales job tasks. Therefore, BI automations can help make business or sales teams’ processes more efficient.

Understanding BI’s Role in Sales

Data collection is an essential component of business intelligence. Yet, companies do not require internal collecting methods to leverage business intelligence. Data can also be obtained from other sources, such as some SaaS companies (for example, HubSpot) or data-as-a-service businesses.

On the other hand, overvaluing information and its acquisition is a major stumbling block for companies attempting to implement data-driven practices in any area. As a result, before even trying to apply enrichment procedures, the first step should be to choose the appropriate data from the massive array of accessible information.

Begin with the Basics

Gradual progress is something everyone can learn from good coding practices. Instead of attempting a total departmental revolution by incorporating all types of data, relevant or not, it is advisable to begin with minor improvements that may have a considerable impact.

Choosing what will have the most impact isn’t always straightforward. With some sales expertise, we don’t need complex data to make an educated prediction. Understanding lead profiles and contacting them based on relevance may be one such estimate.

Despite this, only some firms use data enrichment for inbound leads. It is often assumed incorrectly that whatever data leads have supplied would suffice. Such an approach might be helpful for small companies that receive a few leads daily. When the numbers reach double digits, automatically enhancing leads will be the most efficient choice.

Combining data from an internal with an external database that contains organizational information is a simple example of enrichment. When these two sources are linked, sales teams receive complete business information anytime a new lead arrives.

Utilizing Enrichment

Data enrichment combines publicly accessible data about any prospect with the information currently in a company’s CRM. Data enrichment solutions supplement this data, providing additional insights and context about potential clients. In simple terms, it fills in the gaps in the client information.

Enriching lead data provides additional context making it more likely to convert them. Salespeople may enhance data with publicly accessible information by utilizing some techniques. Employing a scraping tool to automatically collect public online data and upload it into a CRM, manually investigating a lead on a search engine and adding information to the system, or using an enrichment service with its database.

Classification enables salespeople to anticipate the value of a lead. Responding faster and in greater detail will be much easier, resulting in a more robust overall customer connection.

Including Extra Data Analysis into the Equation

The first step of enriching the data of incoming leads with relevant information is using technologies developed in-house or those provided by a third party. While either is a highly effective approach, integrating data science and sales may do far more.

Most salespeople analyze open, and reply rates of the sent emails and measure success according to these metrics. However, this information only shows a fraction of the available receiver’s information.

Analyzing the content of outbound emails and the recipients of those emails is another method that data science can be used to enhance the sales process. The analysis can bring you results including visitor impressions, interest rate, sign-ups for free trials, and if they become a paid client. For instance, if sales emails were integrated with Clearbit software, it would be possible to monitor statistics such as who clicked on the link and did not reply.

With the assistance of a data team, resolving a challenge of this kind might become less complicated. In some situations, they can acquire specific data from industry experts from a third-party firm. For instance, when matching professional data with outbound sequences, it is possible to retrieve information of who (job title) the email will be most relevant to.

The implementation of title tracking will not provide results instantly. Nevertheless, salespeople can identify connections between email open and reply rates and professional data once they have access to some historical data. Over time professional data and email open or reply rates may be compared to provide insight into the efficacy. In the long run, the sales plan could be modified to optimize the effectiveness of cold approaches.

The Future of Business Intelligence

Improving the value and use of data is essential to the future of business intelligence. Never before has such information been so publicly accessible as in today’s big data-driven environment.

With data’s increasing importance as a competitive differentiator, businesses of all sizes will likely increase future spending on their data infrastructure.

Embedded analytics makes data intuitive. Many organizations incorporate BI visualizations inside their apps, allowing users to view analytics without logging into a different platform. Embedded analytics create visualizations tailored to the company’s user interface and daily goals.

Now that many technologies are cloud-based, it allows users at all levels to access real-time data and BI insights, easing decision-making. The most modern BI applications include NLP (Natural Language Processing) querying, which enables users to enter natural language questions processed by AI algorithms. We can expect these technologies to get even more powerful in the near future.

AI and ML will likely extend their automation capabilities, and future BI trends will depend on them. However, like in other sectors, BI will still require some human interpretation.

BI keeps becoming better at helping businesses. When businesses allocate resources to this kind of data processing, they may expect to gain an advantage over the competition, get insight into the behaviors of their ideal customers, and be led in the direction of creating a data-driven enterprise with a solid foundation for growth.

Final Thoughts

Business intelligence is the future, but we can only see it if we embrace that fact. Sales teams must recognize the data’s potential and anticipate the technologies and tendencies.

Sales might be the frontier of real-world data science applications. Profitability is the essence of business. And what better department to optimize using the most recent advances in data science than sales?