On the mind of every business is the dream of having a perfectly optimized sales process. After all, once the performance of a sales department is maximized, revenue generation becomes streamlined and everything else falls into place with ease.
While there are numerous ways to increase the efficiency of sales, one that is underutilized is using data from other departments and APIs to enrich the information about leads.
Most digital businesses collect data. In fact, they collect so much data that most of it is considered “dark data”. Nearly 90% of the acquired information is never used. Instead of leaning out collection processes, putting that data to use, as we see it, is another great approach.
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Andrius Palionis is VP Enterprise Solutions at Oxylabs
Understanding business intelligence in sales
Data collection is a necessary part of business intelligence. Yet, you don’t even need to have internal collection processes to make use of business intelligence. Data can be acquired from other sources such as some SaaS businesses (Salesforce comes to mind) or data-as-a-service corporations.
As previously mentioned, not all data is useful. Overvaluing information and its acquisition is a common pitfall of businesses that attempt to implement data-driven processes into any department. Therefore, before even beginning the implementation of enrichment processes, the first steps should be to pick out the right data from the vast array of available information.
How do we know which data will be useful ahead of time? Think about how the sales department makes decisions. Collect data on important performance metrics. Inefficiencies will be numerous - from how people pick out the preferred leads to the specific communication patterns used to describe products or services.
Starting out simple
There’s something everyone can learn from (good) coding practices - incremental change. Instead of going for a complete revolution of a department by including all kinds of data, useful or not, it’s best to begin with simple changes that might have a large impact.
Oftentimes deciding what will have the greatest impact isn’t easy. However, with some sales experience, we don’t need hard data to make an educated guess. One such guess could be that understanding lead profiles and approaching them based on relevancy would increase efficiency.
Yet, few businesses do any form of data enrichment for incoming leads. An incorrect assumption is being made that whatever data leads have provided themselves will be enough. It might work well for smaller businesses that get a few leads a day. When the numbers start rolling into the double digit territory, enriching leads automatically will be the more efficient decision.
A simple example of enrichment would be combining an internal database with data from an external one that holds organizational information. Connecting these two sources would result in receiving detailed company information whenever any new lead would drop in.
Making use of enrichment
However, just receiving data on a company doesn’t do much for the sales process. Of course, it is certainly better than nothing, but adding brands, companies, and, possibly, other professional data is not making use of data. Making use of data means changing the decision making process.
In order to properly use data in lead enrichment, we should separate them into several categories. Our primary category should be Ideal Customer Profile (or ICP) leads. Such a category should already exist, if not in the sales department, then in the marketing department. Often, we can automate category ascriptions to make things easier for our teams.
Such a categorization allows sales personnel to see the value of a lead ahead of time. Responding faster and with more detail will be significantly easier, leading to an overall better customer relationship. Comprehending the company’s profile, business model, and other important factors also aids in developing a better understanding of their needs.
Adding more data analysis into the mix
Using internal or third party tools to enrich incoming lead data with relevant information is just the first step. A very powerful step, however, the combination of data science with sales can achieve even more.
Another way to optimize the sales process through data science is to analyze outbound email content and recipient (separately). Intuitively, we understand that reaching out to certain people within a company might grant a better closing opportunity. Without the proper data, all we would have is guesswork.
Solving such a problem is quite simple with the help of a data analysis team. In some cases, acquiring data on industry professionals from a third party company might be an option. If such information is available, matching professional data is as complex as setting up a few filters in any tracking tool.
Applying title tracking will not provide any instant results. However, with some historical data, salesmen will be able to see correlations between email open and reply rates, and professional data. Over time, with enough granularity, professional data and email open or reply rates can be matched to grant insight into approach efficiency. Eventually, sales strategy can be adapted according to the data in order to maximize cold approach efficiency.
Data is not just for marketing or tech teams. Sales teams can glean tremendous benefits from data analysis. By performing some analysis based on internal and external data, sales strategy hypotheses can be tried, tested, and applied.
If anything, I believe sales could be the frontier of real-life applications of data science. It’s the essence of business to turn a profit. And what better department to optimize through the latest achievements in data science than sales.
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