In business sales, the most persistent question is how to get more leads, sell more products, and get the most from what we have.
The same applies to data buyers, no matter their industry or location. This especially becomes evident when you have already had data for quite some time, and it begins to seem like there’s nothing more you can get from it. And here’s where you’re wrong.
I promise that after reading this article, you will no longer use company (or firmographic) and employee data the same way. In the worst-case scenario, you will confirm that you’re following the best practices, dodging the worst ones, and adopting the least expected.
While I’ll focus a bit more on HR tech platforms and HR teams, the following advice will benefit businesses from all walks of life.
What is employee and company data?
I’ll make a short intro for those still new to big data leveraging. Save from business and people contacts, company and employee data are two of the most sought-after datasets. While the first two make reaching out easier, the last two make reaching out worthwhile.
That’s because contacting an A-Z list of companies is nothing but cold calling or even a way to get your phone number or email blocklisted. But if you filter your leads by location, industry, and other factors, you and your potential client suddenly have something to discuss.
Overall, company data is precious even without contacts, which are usually publicly available. Heck, even employee profiles use one social network or another. And if you’re trying to catch some VIP, writing to someone from his connected circles might do the trick.
While employee datasets are naturally much larger, one can use them to complement company data. That’s because it enables deeper business-level insights, such as team composition analysis or discovering key employees. In a best-case scenario, you can merge these databases.
Yet that’s just the first level on the journey through the rabbit hole. So why stop with employee and company synergy? To leave the competition behind, add job listings to create a profile of an ideal candidate. But I digress.
What you may not find in your employee or company dataset
One of the most common issues I see with fresh data buyers is that they expect everything in some neat spreadsheet that is easy to filter and compare with hundreds of millions of records, but that simply cannot be the case. Even filtered and enriched data, also known as clean data, requires some help from a data analyst or data engineer to make sense.
The second false assumption is that such data will include contacts. Unless specified, emails require extra investment.
You may also not find data quality. If it’s outdated, inaccurate, and non-standardized, you will struggle to get results even if you avoid data mismanagement. The dataset might also be too small, especially if you need a macro-level analysis. It may suffice to find candidates in a specific city or state, but seeing the global tech sector recruitment tendencies will take more than that.
Last but not least, don’t put an equality sign between data richness and data quality. Poor data means few data points, while poor quality means data points riddled with unintelligible or plainly wrong input.
How to best use employee and company data traditionally
Most of you probably know and cultivate these time-proven tactics, but I still want to remind you about a few you may have accidentally forgotten.
First and foremost, HR representatives will benefit from enhanced talent sourcing, especially if it’s done with the help of AI.
When the data is fresh, filtering by employment length, experience, education, and other publicly accessible factors will ensure you’re targeting the right candidates. And with the help of firmographics, you’ll see which sectors are booming and will soon need an extra workforce.
If you’re into investing, employee data can show the talent movement and which companies attract the best talent. Combine that with your company dataset, and now you have two sources pointing in the same direction-your direction.
Furthermore, both categories are invaluable for lead enrichment. Employee data will fill in the blanks and make qualification faster. In the meantime, company data will let you map specific areas where those leads tend to flock.
A traditional example
You’re a recruiter for a tech company with the task of hiring 50 on-site senior developers. You open your employee database and start by filtering candidates with more than 5 years of experience. However, the pool is not deep enough unless you leave remote options unfiltered, so you lower the expectations to 3 years or more.
There’s another problem-just a few currently hold a senior position. So you check the education line and see that most developers who work in the top tech companies (including yours, of course) and have 5 years of experience are actually from the same university.
Seeing this as a positive sign, you filter less experienced candidates to those who graduated from the aforementioned institution. Just to be sure, you also check if the youngest senior developers also attended the same school and put your company in the position to have the best talent in the foreseeable future.
To conclude, everything will be alright if you follow these tips, but the apple will stay on the Tree of Knowledge unless you shake it well. Read on to learn how to do that.
How to avoid firmographics and employee data handling pitfalls
Big data veterans can skip this section-there’s nothing new here for you. Except you’re not that happy with the results you get from using all those datasets. The first advice comes before you even access the database.
As Infoworld warns, having data ponds instead of lakes will lead to multiple analysis results, especially at the enterprise level. If neither of your departments has the full picture, all you’re left with is a broken frame. And I’m not preaching the all-eggs-in-one-basket approach – not having copies (not a copy!) of your database is akin to wearing pants with no underpants.
I shouldn’t be saying this, but here it is: don’t buy a dataset just because everyone around you is buying one. First, determine what goals it should help you achieve and whether that will have ROI, given that you’ll need at least a part-time data analyst and time for analysis. The worst you can do is buy a dataset, hire a data analyst, and start thinking about what to do next.
Even if you have the plan ready, don’t expect this data approach to work all the time. Ads don’t work all the time. Ads backfire. The same is true with your data.
So, to avoid this, follow the experts’ advice, like this from Athena Solutions, and look for a solid provider and experienced analysts.
Don’t let greed overshadow the need
More money is better, but this doesn’t apply to data. More data means more money spent on handling and analyzing, more errors, and paying more for one mistake.
So, if you’re not up to some megalomaniac business plan, determine what you need first and then look for the data provider. If you need to form a new sales team, get your city or state dataset instead of a global one. Filter unwanted professions and optionally enrich them with extra company data about their current employers and what they can’t offer that you can.
Once again, remember that drawing broader conclusions from limited data is doomed to fail.
Trendy or stylish?
According to BairesDev, following the trends is not considered dangerous unless you’re in a business.
Just because everyone is getting that broccoli haircut, you’re going to get it as well? The same works for any big data trends. If you’re happy with your current software and datasets, stick to it. Not everything works for everyone, just like the broccoli haircut.
At this point, you’re brave enough to shake the Tree of Knowledge, but the apple keeps hitting your head, and you haven’t had a taste of it yet. Join me in the next chapter, where you finally get to take a bite.
How to best use employee and company data untraditionally
Coming up with bizarre ways to use big data becomes more difficult the more macro you go. And that’s what I’ll stick to because niche ideas work for niche cases and sometimes only for your own company.
Firstly, squeezing something extra from employee and company data is unnecessary. This can be left as an experimental and extra-curricular activity, provided you have enough spare hands.
So don’t fear missing out if you never try it, but be aware of such opportunities. Hopefully, these seven ideas and examples will help your business in some way.
1. Dataset combinations
When someone asks me which dataset I should buy to maximize ROI, I suggest analyzing data points. Start with something big like employee and company datasets and check the data points from others that could be of interest to you. Then, you decide whether those extra records are vital, needed, or nice to have.
A good example from the HR industry is GitHub and similar repositories. Say you’re assembling a new developer team and choose to filter the best candidates from the main employee database. Now, add GitHub data and see how their code ranks, if it’s even there.
This way, you get not only a CV but also a portfolio. Yes, this might only work for the enterprise level, but there’s an alternative in, for instance, getprog.ai that does just that-offering IT professionals scored according to their code quality. In the end, what you need is not a diploma and not necessarily work experience.
2. Feed your data department
There’s a saying among data analysts – “Give us everything, and we’ll see what we can do.” I couldn’t agree more.
Too often, managers come to data people with their own stats and look for approval and data expansion. Guess what? It works the other way around.
Instead of doing some “analysis,” give them all the data you have and ask to look for ways to increase leads or target a more specific audience that looks like ICPs.
Any constraint like “Let’s check only employee data first” or “Focus on the East Coast – that’s where our clients are from” hinders the data team and your company because it reduces the chance of finding something unusual but useful.
3. Identify influencers and map relationships
As we all know, the hand washes the hand, and the more people you know, the more power you have.
When building a lead or future candidates database, check employee data and see who works or used to work with whom. Even if they’re not in each other’s inner circles, chances are they know that person and can tell something about them. If you target the person with the most acquaintances, you increase the chance they will tell you about your job ad or your product to the others.
Moreover, finding someone who can introduce you to a potential client is always worth the effort. Given the size of a typical employee database, you might find even a few!
After such analysis, your HR people can create an evaluation system similar to what getprog.ai did, as mentioned previously.
I remember one example from our client, which mapped influencers of a particular social network to filter those with the most connections. Then, they targeted these people with specific political ads and got a better ROI instead of targeting as many influencers as possible.
4. Is this data for real?
Just like a politician can help identify a corrupt politician, data can help you identify fake data. Your HR department may have noticed that some businesses constantly post job ads even though they don’t seem to expand that fast, unlike gas.
When updated daily, company data can help easily identify these job ads as fake. Their only goal is to make the candidates and competitors believe this business is thriving.
Now, you can switch from manual to automated work and get a list of such sinners for future reference. And it’s up to you to report this to the job ad platform.
5. The University of Success
People in your employee data were not always employees. One way your HR people can know whether one candidate has an upside is to look at the current senior-level workers and check their education. Chances are that the best ones attended one or another university.
With such a correlation, you can decide which candidates will perform better in the long run. At the same time, you can see if there are any tendencies in what your competitors choose. The top-ranked universities may not guarantee the best employees.
With the same employee and company data, you can even come up with your own university ranking for IT, Management, and other professions.
6. New hires vs requalification
Let’s say the need for AI Prompt Engineers is on fire (which soon becomes a reality). The market has nothing to offer, and the demand keeps on growing. Once again, it’s time to open that employee database.
Now, find people currently working as Prompt Engineers and check what they did before. If most of them were Data Managers, you could focus on contacting their ex-colleagues and offering requalification courses.
While such an offer could be attractive in itself, learning that your ex-colleague has been working in this new position for over a year may impact their decision.
7. Check other data with your data
By the time you get comfortable with your employee and company sets, you will likely have built a custom dataset for yourself. That means you put together only the relevant data points and reduced the number of irrelevant records.
Now, you can tell if the correlations in the original data match the ones from your custom dataset. Working with a cleaner dataset also takes less time and reduces the chance of errors. Let’s illustrate the point with this hypothetical but realistic scenario.
Imagine waking up and checking the news only to find an authoritative outlet warning about the shrinking market and advising to adopt austerity measures. Now you have two options.
You either go sheep mode and reduce your next quarter’s spending, aiming at survival. Or, you can go deep mode and check whether this applies to your market. If the competition is hiring by dozens, building new offices, and increasing revenues, chances are you should also keep doing what you do.
Otherwise, emotional reactions with no data to back them up can easily lead to a self-fulfilling prophecy.
Finally, you’ve tasted the apple of the Knowledge Tree. Was it tasty? Let me know in the comments below.
Bottom line
Not everyone who buys employee, company, or any other database knows how to make the most of it. Following the best practices will be enough for the majority, but knowing how to avoid common pitfalls is of the essence to the big data debutants.
And what about all those unusual or weird ways to leverage company and employee data? Well, this should only happen if the other two are already in practice. That’s because it involves a greater risk of wasting time, and not all businesses are ready for that.
Whether you’re in HR, Sales, Marketing, or any other department, I want to repeat one piece of advice: Give all the data to the analytics team and let them work. That’s the best chance to taste that apple without it hitting your head first.
This article was originally published on Datafloq.
