In the age of marketers being data-driven, we all understand and recognize the role of data, not only in identifying opportunities but also in closing a sale. So, the quality of data determines the insights derived out of it and more importantly how the information is used.

If you look closely, there are a few parts to this:

Quality of Data (Need for Data Enrichment) Insights and Signals Flow of data and insights across the organization

Great marketing campaigns or a relevant sales call stems from having access to the right data, in the right place, at the right time. Let us explore, each of these sections in detail.

Quality of Data (Need for data enrichment)

On an average, B2B data tends to decay at 30% or more every year. In other words, 30% of your business contacts can go out of date each year; including key company information, details about the org structure, and most importantly, accurate contact information.

This doesn’t just stop with your contact database getting smaller. Inaccurate data means marketing messages are sent to the wrong people, sales professionals waste their time trying to reach prospects who aren’t sales ready. Also, it can lead to misclassified prospect segmentation and reach out tactics.

We need a holistic approach to enrich data and maintain data integrity. For example, at Fiind, our AI-driven sensors constantly look for updates in data or events, thereby activating a bot that knows where to look across the web to get the maximum value.

So, as a result, what starts as an email or domain name transforms into an enriched organizational profile, with not only actionable firmographic information but also customer signals on buying intent and product fit to identify your total addressable market and serviceable market.

Insights and Signals

Most companies tend to look at data enrichment only from “filling up the data gaps” standpoint, and mostly it is firmographic information.

But to derive insights, we in our customer journey have realized that firmographic information such as location, industry, revenue, etc. can add little value. To make the data enrichment exercise really work, you need to go beyond firmographics. You need signals that indicate a prospect’s intent to buy your product, their organizational fit with respect to your offering and more.

Let me discuss a signal as an example, to explain this. Let’s say, we are looking at the hiring signals of a targeted prospect company. And let’s say the dataset collects information on company’s hiring trends, such as:

Open positions added in the last week

Total jobs posted in the given time period

Roles being hired, etc.

When you combine this information with other identified signals such as technology used, funding, employee size, and revenue – these signals become highly context-rich information.

For example, if you put this insight in the context of selling a CRM, and you see:

that the target organization is hiring a lot of salespeople, they recently got funded, and you know that your CRM is a perfect fit in their tech stack

It means the data enrichment has not only given you a broad idea of whether the given organization is a good fit for you, but also a clear indication of the right time to pitch in your product.

Flow of Data and Insights across the organization

Lastly, it is not enough if you have highly enriched customer data. You need to make sure that the enriched data flows seamlessly across the organization. Fiind Data enrichment platform integrates with business applications such as CRM and marketing automation tools across the organization. Thus, the flow of the enriched data into your business apps is seamless, without the need for manual updates. And most importantly, there are regular data health checks, and structuring of unstructured data before publishing it across the integrated ecosystem.