Data work has long been part of B2B marketers' daily bread. You should definitely keep an eye on the data quality, because today it has a decisive influence on the overall success of the company.
In B2B marketing, you strive to address customers personally, provide individualized content and try to improve the customer experience. High quality data is an important prerequisite for this. But even as B2B marketers increasingly embrace data-driven strategies like account-based marketing (ABM), they often aren't confident in the quality of the data they work with every day. We took a look at what the status quo is, how bad data happens, and why data hygiene is so important to marketing success and overall business success today.
When it comes to B2B data assets and their quality as perceived by B2B marketers, it's entirely appropriate to call it a crisis. According to a recent study by CapGemini , just 27% of executives are satisfied with data quality and only 20% trust it.
So many B2B marketers are unhappy with their data situation and confidence is falling. This is dangerous in an environment where data is the foundation for success
Studies had already predicted for the year 2020 that the customer experience will replace the price of a product as the most important differentiator. In B2B, your company cannot afford to jeopardize the design of this customer experience through poor data records.
In B2B, if you want to better understand your customers, anticipate next best actions in the spirit of Next Best Action, and design a Customer Journey perfectly around the customer's needs, you are imperatively dependent on data from multiple sources. And that data needs to be:
accurate
complete
up-to-date
and of the highest possible quality
The dissatisfaction of marketers with the data quality is therefore a warning signal that you should definitely take seriously
The best algorithms and modern approaches in machine learning will be ineffective if you run your software on data that contains errors and has other problems
Modern technology-enabled B2B marketing is sophisticated. But before you can worry about advanced aspects like your marketing technology stack, you need to get the basics right. And that starts with working on data quality.
In B2B, you work with a wide variety of data from numerous sources. The data comes from either public or private sources. You can collect data via a contact form, or you can publicly view and collect other data directly on the customer's website.
Among the most important data are the addresses of your contacts. It is inevitable that you collect such information not only once, but several times. This is where problems arise, such as the frequently observed duplicates. These data sets often differ in details, for example because someone copied an e-mail address incorrectly or changed jobs. It is necessary to determine the correct address and, if possible, to remove the duplicates from the database. The keyword is data cleansing.
Problems also arise because you're working with a variety of different systems, and the data is stored in different formats. In some cases, this makes working with data sets much more difficult and mistakes happen more quickly.
Another problem is that employees or departments in the company depend on receiving the data in a certain format. However, often the data is in a format that is not suitable for further work and cumbersome conversions are always required. This happens when the departments and teams for data entry and data processing are not aligned.
Forms are all well and good, but when it comes to data quality, all you can do is add a few controls, such as
Email Format Check
Mandatory fields
Placeholder texts for explanation
Pre-selection for standardization,
Completely automatically check whether qualitatively fits, but unfortunately can not. That's why you should schedule a regular data check and cleanup, especially for leads generated via a form or a bot.
Also, make sure that you receive updates from the client to update the records once recorded. For example, you can track the LinkedIn profile and set it so that you receive an automated notification when you change jobs. Another option would be for you to give him forms to fill out with the most important data fields with the existing values. This way, if your leads have accepted the cookie, they don't have to fill it out again, but they will most likely check the data and adjust it if necessary
You shouldn't underestimate how massively poor data quality affects your company's success. Below we list some typical problems that unfortunately occur in B2B marketing again and again and what consequences they have for your company:
This is almost a classic mistake in B2B marketing and can clearly be traced back to insufficient data quality. You play out a marketing campaign, but the address intended for it no longer exists at all
The main problem: The customer may actually be interested in the product, but you don't have his current email address. This means that you have had to pay for a marketing campaign for nothing and you have not reached the contact that still exists.
A little tip: Monitor your email lists - especially the automatic emails - regularly for contacts that are "bounced". You should then update these contact entries accordingly before you send 5 emails that are all undeliverable
There is no added value in storing the same data over and over again. This unnecessarily inflates the database and causes performance and storage space problems. It is also typical that a customer is the addressee of the same marketing campaign several times due to the multiple storage of his address. This causes unnecessary costs and annoys the customer.
The required data exists in principle, but it is not structured correctly or in the optimal way. This often means that you can't work efficiently with your systems. This also causes unnecessary costs and wastes valuable resources.
Decisions in a company are based on data. If this basis is insufficient because the wrong data is stored, you cannot make good decisions. For example, low data quality hinders process optimizations and business development. Low data quality also has a negative impact on transformation processes. For example, reaching the next level of digitization often involves significantly more effort than is actually necessary.
Another typical problem that we often observe in practice is the lack of evaluation of the data. If you don't analyze and evaluate it, you don't know what worked and what didn't work. Accordingly, you cannot make strategic or data-based decisions for the future.
If you want to significantly improve the quality of your data for B2B marketing, it is important to adhere to rules when handling the data
The most successful marketers are committed to strict data hygiene and benefit from it for all their future work in B2B marketing
Data hygiene is therefore not a one-off measure, but an ongoing process. To establish this, the following steps are recommended:
Designate someone who is responsible for data quality assurance in your company. The task is too important to do without dedicated responsibilities at this point
Make sure these data quality officers work closely with all relevant teams. This doesn't just include marketing and sales. You should also involve IT, data analysts, and all those who work on and with the data.
Store marketing and sales data in one place: it's common practice for marketing and sales to rely on their own solutions for their CRM and automation. In order to be able to create the so important personalized content for your B2B customers, however, storage in a common place is advantageous.
Make sure to simplify the data entry system to prevent data entry errors. Define standards that are mandatory for data entry and storage. And train your staff and teach them how to use the data entry systems properly.
All marketers today deal with a wide variety of data from a wide variety of sources. It's critical to maintain one's data and keep the quality at the highest possible level. Smart companies standardize their data and set rules for handling it. Errors can never be completely eliminated, but they can be minimized through a structured process. To make this happen, you need to think about data quality and conduct training to make your employees aware of data hygiene.