SeveralsaleExecutives know the benefits of relying on customer insights to make strategic business decisions. Most organizations have a massive amount of information on hand, and thatgivenIt can help with everything from putting together a strategy to improving the bottom line.
Useful data can be as simple as the number of calls a salesperson has placed with a particular customer, or as complex as analyzing transaction records across entire product lines to uncover hidden patterns and better understand trends and opportunities.
However, many executives do not use this data. They don't have a clear understanding of where to start when analyzing internal and external information.
This is especially true for sales teams without a data analytics background. In the past, only IT professionals used business intelligence (BI) software. However, BI technologies have become more user-friendly and intuitive, allowing for widespread use across a variety of business sectors.
More in salesTraditional vs Conversational IVR: What's the Difference?
Business Intelligence vs. Artificial Intelligence
Before we get into sales efficiency, let's'define sBusiness Intelligence (BI)Compared toArtificial Intelligence (AI)). While these terms are sometimes used almost interchangeably, a more accurate way of thinking would be to treat BI and AI as separate but complementary technologies.
“Intelligence” in AI refers to computer intelligence, while in BI the same term means intelligent business decisions that can be enabled through data analysis. BI can help organizations organize and act on the massive amounts of data they collect.
However, these solutions fall short of helping managers provide the reports needed for data-driven decision-making. A solution to this challenge is the integration of a tool (BI) that enables better decision-making.
While there are various methods of increasing sales productivity, integrating and integrating data from other departmentsAPIImproving the information of potential customers is used very little. Integrating data enrichment and analytics into sales can help companies achieve fantastic results.
after aHarvard Business Review-Studie, existing BI technology can automate 40% of the time spent on sales work tasks. So, integrating this automation can help make business processes or sales teams more efficient.
How is BI impacting sales?
Data collection is an essential part of business intelligence, but companies don't need internal collection methods to use it. Data can also be obtained from other sources, e.gSaaSEnterprises (e.g. HubSpot) or data-as-a-service companies.
On the other hand, the overvaluation of information and its acquisition is a major obstacle for companies trying to implement data-driven practices in every area. Therefore, before even attempting to apply enrichment techniques, the first step must be to select the appropriate data from the multitude of information available.
How to use data to increase sales
- Select the relevant data.
- Use data enrichment.
- Add additional data analytics.
1. Select relevant data
Like good coding practices, data-driven sales insights should be a game of incremental progression. Rather than attempt a total departmental revolution by incorporating all types of data, relevant or not, start with small improvements that can have a big impact.
While it's not always easy to decide what will have the greatest impact, with some sales experience you don't need complex data to make an informed prediction. Understanding potential customer profiles and contacting them based on relevance can be one of those estimates.
Despite this, some companies only use data enrichment for inbound leads, under the mistaken assumption that whatever data those leads provide would suffice. This approach can be useful for small businesses that get a few leads every day. However, when the numbers get into double digits, the most efficient option is automatic lead growth.
Combining data from an internal database with an external one containing organizational information is a simple example of enrichment. When these two sources are linked, sales teams receive complete business information every time a new lead arrives.
2. Use data enrichment
Data enrichment combines publicly available data about each prospect with the information currently in a company's CRM. Data enrichment solutions complement this information by providing additional information and context about prospects. Put simply, it fills in the gaps in customer information.
Enriching lead data provides additional context and makes conversions more likely. Marketers can enrich data with publicly available information using a few techniques, such as: B. Using an extraction tool to automatically collect public data online and uploading it to a CRM, manually researching a lead in a search engine and adding information to the system. or use an enrichment service with your own database.
Ranking allows sellers to anticipate the value of a potential customer. Responding becomes much easier, quicker and with more detail, resulting in stronger customer loyalty.
3. Includes additional data analysis
The first step in enriching incoming lead data with relevant information is to use technology developed in-house or provided by third parties.Although it is a very effective approach to integratedata scienceand sales can do much more.
Most marketers look at the open and response rates of the emails they send and use these metrics to measure success. However, this approach only displays a fraction of the information available from the receiver.
Analyzing the content of sent emails and the recipients of those emails is another way that data science can improve the sales process. Analytics can show results including visitor impressions, interest rates, free trial signups, and whether leads convert to paying customers. For example, when sales emails were integrated with Clearbit software, teams could track statistics such as: B. who clicked on the link but didn't respond.
With the help of a data team, solving this type of challenge can become easier. In some situations, they may purchase certain data from third-party industry experts. For example, by combining business data with outgoing strings, you can get information about who (in terms of job title) the email is most relevant to.
The track tracking implementation does not provide immediate results. However, marketers will be able to identify the connections between email open and response rates and business data once they have access to some historical data. Over time, you can compare deal data and email open or response rates to gain insight into effectiveness. In the long run, you can change the sales plan to optimize the effectiveness of cold approaches.
The future of business intelligence
Improving the value and use of data is critical to the future of business intelligence. Never before has this information been as accessible to the public as it is in today's big data environment.
With the growing importance of data as a competitive differentiator, companies of all sizes are likely to increase spending on their data infrastructure in the future.
Built-in analytics make data intuitive. Many organizations embed BI views in their applications, allowing users to view analytics without logging into another platform. Built-in analytics create custom views for your business UI and daily goals.
With many technologies now cloud-based, users at all levels can access real-time data and BI insights, simplifying decision-making. The most modern BI applications includePNL(Natural Language Processing) that allows users to input natural language questions that are processed by AI algorithms. We can expect these technologies to become even more powerful in the near future.
esMLIt will likely expand your automation capabilities, and future BI trends will depend on it. However, like other industries, BI will still require human interpretation.
BI continues to improve to help businesses. When organizations allocate resources to this type of computing, they can expect to gain a competitive advantage, gain insights into their ideal customer's behavior, and move toward building a data-driven business with a solid foundation for growth.
Sell smarter10 Website Design Elements to Boost Sales and Engagement
Maximize sales with business intelligence
Business Intelligence is the future, but we can only see it if we accept this fact. Sales teams need to recognize the potential of data and anticipate technologies and trends.
Distribution can represent the frontier of real data science applications. Profitability is the essence of business. And what better department to be streamlined with the latest advances in data science than sales?