How to Validate the Accuracy of Data in an Intent Data Bank?
An Intent Data Bank can be useful for businesses seeking to discover high intent prospects they can market to. At the same time, the usefulness of this data hinges on its accuracy. Flawed and obsolete data, for example, results in squandered resources, lost opportunities, and erroneous decisions. It is, therefore, critical to devise strategies that enhance the reliability of intent data through data quality, relevance, and accuracy measures.
1. Verify Data Sources
The first step in validating intent data is ensuring it comes from reputable and varied collection sources. Intent data is often captured through web traffic, social media interactions, or purchased from aggregate vendors. It is important to determine whether those sites are reputable, current, and adhere to legal data collection practices. Using reputable sources to back data can eliminate fraud and errors.
2. Assess Data Freshness
Data is considered more valuable when it is current and applicable to the scenario. Buyer intent can shift from one prospect to another within a short time frame, so out of date information has the potential to misrepresent an account’s interest level. Businesses should set guidelines for data expiration to make sure that signals retrieved weeks or even months ago are twiddled with or removed. Initiating regular updates from possibilities stated earlier, alongside data wiping programs, can ensure freshness of the Intent Data Bank.
3. Assessing Behavioral Accuracy
Determining measurement of high purchasing interest from one visit to a pricing page is weak, which is expected. So, what can aid in confirming strong buying intent? One strategy would be repeatedly analyzing patterns through touchpoints over a sustained period. Moreover, confirming their interest through multiple engagements can assist in setting an authentic intent.
4. Measure Against CRM and Sales Intelligence
Outreach sales teams come in direct contact with prospects, a huge portion of the work gives them valuable unbiased information. Aligning this with intent data and CRM records will help in determining if anything rings true within the scope of sales records. If the information proves contradictory then it shall be imperative to further examine the data collection framework.
5. Apply AI and Machine Learning for Accuracy of Data
True-positive data filters detect anomalies. With Machine Learning comes improved quality of intent data, which requires actual business resources. Even as businesses set up more advanced predictive models, the accuracy of the data validation effort can be improved.
Precision of Intent Data Bank concerning intent is of utmost importance for strategic marketing and sales. While executing these measures, companies may substantially enhance reliability of the intent driven strategies.
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