If You’re Not Scraping Data, You’re Losing Revenue

Author: 
Greg McBeth

Any sales leader would love to wave a wand and instantly find good-fit companies with interested buyers.

Although building a successful pipeline and closing deals are hardly things that happen instantaneously, the process doesn’t have to be entirely arduous. With modern tools like data scraping, sellers have access to a wand – no magic required.

Getting the most out of readily available data

Admittedly, data scraping isn’t a new concept. Anyone who has downloaded names and addresses from a public directory has “scraped” the content. However, scraping doesn’t seem to be on many executives’ radar as a powerful way to stay competitive.

Case in point: NewVantage Partners annually surveys C-suite participants to understand market trends and associated corporate strategies. The firm’s most recent research indicated that 99 percent of respondents want to improve upon the strategic use of online data. But only a third of them are close to that goal, an indication that most aren’t scraping the web for insights.

The simple truth is that few sales teams understand the full power of data scraping. For example, their salespeople haven’t discovered that scraping allows them to understand the words and phrases associated with an ideal customer by sifting through internet clatter to identify keywords that could lead to qualified prospects.

Another advantage of data scraping is investigating changing markets. You can do this by scraping recent news or social media or tracking shifts in typically static places like websites.

Truly, the opportunities to do everything from identifying possible new hires or communicating in a more personalized way during the sales process are game-changers — at least for those in the game.

Working data scraping into the sales flow

Not currently data scraping? Your timing couldn’t be better to get started. Here's how:

1. Gather your initial data points. Whenever you plan to take ownership of a new initiative, it’s much easier to get cross-functional buy-in with some data to back up your claims. With web scraping, it’s important to be able to demonstrate the impact of specific data points on your ability to close business.

One approach is to evaluate how missing data affects your ability to identify high-quality sales prospects. For instance, if you know that a customer champion who moves to a new company is likely to bring your solution to his or her new company, an inability to identify those company moves will cost you precious revenue. Gathering this impact data, both quantitatively and qualitatively, will significantly bolster your ability to drive change within your organization.

2. Build your case and educate corporate leadership. Non-sales and sales executives alike often have tunnel vision when it comes to selling and, as a result, miss opportunities to implement exponential levers to action. For instance, they believe building an effective protocol involves adding representatives, dialing more numbers, and sending more emails to more people. Although those tactics can have a positive effect on sales, they can also be costly (literally and to a firm’s reputation) if improperly utilized.

Concentrating on data scraping instead allows a sales team to use existing data to increase potential outcomes efficiently with a comparatively small impact on operational excellence. However, it’s important to get at least a few champions at the executive level to ensure buy-in for a scraping budget and related resources.

3. Create your initial test plan. Every business has limitations, yours included. Pull together a “wish list” of data to move the needle for your sales team by leveraging information about your current client base, with clear test plans and key performance indicators to measure impact. Only after you solidify your hypothesis and test plan list should you make an investment into scraping data to meet your needs.

I once worked with a client that made almost 80 percent of revenue from upsells and expansion. The client's most important goal was to find more customers likely to have high buying potential. Consequently, we built a hybrid model that scraped job sites to determine the number of open positions and then analyzed those positions relative to company and team size. The ones that had the highest likelihood of growing were considered good fits for our client.

4. Find an in-house scraper, build a scraper yourself, or buy scraping software. During the early scraping stages, focus on leveraging only the most important data. Are those data points available publicly and easily accessible at the scale required? Consider scraping them yourself. Or hire a remote team to complete the task for you.

If it’s a little tougher to unearth what you want, or you don't have the resources to do it, get a technical colleague to build an automated solution. And don’t forget to include in your scraping plan how team members can access the data, how often it will be updated, and how you will ensure a positive ROI.

Uber scrappily used a combination of internal and external data scraping to estimate its competitors, gathering tons of data in-house and then supplementing it with purchased information. All the data points were explored via machine learning to determine how much market share competitors held, as well as how much those competitors were spending to get employees and clients.

5. Validate or reject hypotheses against meaningful key performance indicators. The most exciting hypotheses don’t matter if they don’t correspond to reality. Consistently evaluate your scraped data against metrics you care about to see whether you’re generating real results. Without a solid test framework to validate your hypotheses, you can’t ensure you’re gathering useful data.

Not sure how to do this? You can always measure simple correlations of scraped data points against the KPIs you care about, particularly if you have a reasonably sized total addressable market. As you become more sophisticated, you can seek out more robust analytics or leverage machine learning to determine the precise impact of specific data points on revenue and other KPIs.

6. Adjust your strategy regularly. Achieving early success with your data scraping? You’re not alone. When asked, 73 percent of NewVantage Partners survey participants said they had already seen benefits from their data-based initiatives. However, it’s critical to never rest on your laurels. Instead, continue to evaluate whether your scraped data is providing enough return on investment to justify efforts and opportunity costs. At a minimum, reevaluate your modeling and scraping methodology once a year.

Data scraping isn’t something only tech geniuses or veritable data magicians can do. It’s a reliable strategy available to sales leaders and their teams in any size company. The key is to do it purposefully and methodically to get the highest returns.

Greg McBeth is the head of revenue at Node.io, the first AI-infused discovery engine that identifies relevant, personalized opportunities for people and companies.