porating 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 being 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. But 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 own 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. Adding extra into the equation The first step of enriching the data of incoming leads with relevant information is by using either technologies developed in-house or those provided by a third party. While either is a highly effective approach, the integration of 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 on industry experts from a thirdparty 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 will be able to 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 BI 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 (machine learning) will likely extend their automation capabilities, and future BI trends will depend on them. However, similar to other sectors, BI still will require some human interpretation. BI keeps becoming better at helping businesses. When businesses allocate resources to this kind of data processing, they can 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. 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 realworld 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? o Andrius Palionis is vice president of enterprise sales at Oxylabs. CHANNEL MANAGEMENT 66 CHANNELV ISION | MARCH - APRIL 2023
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