Using (Big) Data to Improve Department Efficiency

Julius Cerniauskas June 22, 2021

There’s enough data out there for everyone and everything. Current capabilities in public web scraping allow businesses to acquire information on basically any aspect of the entire value chain. In-depth public information can be procured about products, customers, competitors, markets and everything in between.

Yet, most businesses follow old-school practices that include some amount of guesswork. Data is widely used in departments like SEO and PPC but in others the adoption of information-driven decision making is often slacking behind. In some cases, data-driven is just a fancy name for looking at data without understanding the meaning behind it.

Data for non-data people

Of course, it’s easier for SEO or PPC people to speak about being data-driven. Their work is often directly measurable through numbers. For example, effectiveness of ad performance can be easily measured through ROI and other metrics.

How do we measure the quality or effectiveness of, say, content management departments? Writing is often a little esoteric as we can’t even easily define what makes it good or bad, let alone its performance.

Strict data-driven people will often attempt to squeeze content into a few specific metrics. Content-driven people will fight back against the metrics as they will feel their creativity being smothered.

However, all departments in the company have the same goal – to create profit. Using data to measure performance, if possible, is a necessity as it will allow everyone to reach the goal more efficiently. For non-data people, we should be looking for more abstract metrics to supplement the old school data (e.g. view count).

One of the best auxiliary signals for the value of writing is syndication and engagement. Clearly, content strategy won’t deliver as instant results as PPC, therefore measuring the ROI of writing will be a headache. However, a secondary goal of all content is to keep people engaged with your brand. If the content is being shared on other platforms or receives other types of attention, it’s doing part of the purpose.

Yet, to create clear and inspirational signals from such data, both internal and external sources need to be combined. That takes a lot of work, management, and effort. However, the end-goal creates a better working environment for both the department and entire company.

Preceding with data

There’s this saying attributed to Thomas Aquinas – “a small mistake in the beginning is a big one in the end”. Small misunderstandings or inefficiencies can lead to overburdened processes and actions. Essentially, that’s what Kaizen is used for. We can use data for the same thing in the digital space.

Instead of looking at data as a predictor of (past) performance, we can think of data as a way to infuse our current decision-making in certain departments. A good example of such an application is sales and marketing.

Sales departments can have professional data infused through CRMs to enrich and optimize the lead management process. If you know your Ideal Customer Profile (ICP) well, leads can be enhanced by combining several services (e.g. like Salesforce) into one. One of the most efficient data points that can be used for is matching company size, industry, etc according to the professional email provided with the lead.

Marketing departments have a lot of abstract tools they use as well. A common practice in the B2B sphere is to formulate Buyer Personas in addition to the Ideal Customer Profile. However, often Buyer Personas are thought to be some abstract representation of a potential buyer throughout his journey.

Instead of thinking your way through, data can be utilized to deliver a clear picture. Combining professional data, current important partners and clients, and their paths to purchase (from sales log and analytical tools) would provide a comprehensive picture. It would include the people who perform the buying purchase, what content interests them the most, etc.

Culture of data

Many people, especially those outside mathematical fields, have a certain aversion to data. At some human level that aversion is perfectly understandable – being measured by some abstract tool never feels great.

At the same time, data is irreplaceable in business. The way to merge these two contradictory things is through fostering a culture and understanding of what being data-driven means. A first step to take is to make information accessible to everyone instead of being reserved to just analytics teams. A closer contact with data makes understanding it easier.

Another very important thing to establish is that data is not about people per se. It’s about the strategies being taken on a micro and macro levels throughout the business. Therefore, data measures the implementation and effectiveness of strategies being undertaken (sometimes, even on an individual level), not the worth of people.

Over time, by fostering a focus on data and understanding how to work with it without stepping on people’s toes, a completely new culture will arise. One where people will be actively looking for more ways to enrich their decision making processes with data.

Conclusion

Entire strategies can be developed and approaches changed through the simple inclusion of data into the operations of every department. The challenge lies in finding a combination of primary and secondary signals within the data to get a complete overview of the field.

However, because business has one overarching goal, everything can be measured. These measurements may be less or more accurate but they do exist.

Data management for business purposes doesn’t end here. At my company, Oxylabs, we will be hosting OxyCon, a two-day event on August 25-26, dedicated to the web scraping and data industry. This year we will be going over three topics – the future of scraping, data collection for business purposes, and scraping for developers.

Get your free OxyCon tickets here!

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