The quality of your lead data is the ultimate determiner of the success of your marketing campaigns. The way incorrect data generates wrong leads, the same way accurate data offers you right leads, result oriented sales funnel and actionable insights.
Next, the process of lead scoring is accelerated – right demographics, psychographics and geographical data allows you to fast assign quality points to each lead. It empowers you to devise personalized campaigns, creating gateway for long-term engagement and positively impacts conversion statistics.
While having a clean, accurate and up-to-date lead database is an imperative; adopting ways to ensure accuracy of your lead data is a daunting task. Below, we discuss on how to cleanse your lead data and boost engagement.
Data-led decision-making has made sales and marketing professionals look for best insights from data experts who constantly try to find out best ways to address most pressing questions like how do you cleanse your data and why do we need to clean data. Here are answers to a few of them:
50% of organizations believe that more than half of records in an average B2B database are not standardized and validated.
There’s no escape for sales professionals from checking data correctness, as incorrect data disturbs the entire sales strategy – campaigning and promotion – as the data doesn’t showcase accurate details of target audience. Besides, consuming unnecessary cost, bad data also tarnishes your brand’s image.
There are two effective ways to ensure data accuracy, validity, and reliability. First, initiate cross-check mechanism in the entire data processing pipeline. Second, regularly, drive internal and external data audits. Regular data audits help you:
Quirky formats and field values are characteristics of a lead database and dirty data can make analysis and reporting - a nightmare. 80% of efforts in a typical quality data analysis get wasted in standardizing and normalizing the data.
In order to ensure that data can be modified, or separated as a portion, and combined as needed, each field (column in a database), must uniquely represent a single concept related to customers.
Data standardization means:
As part of validation, ensure that there are no redundancies in your data. Check out that there are no intricate and complex relationships caused by numerous tables. Correct linkages amongst different database tables are essential and the foremost condition to extracting data for sales analysis.
Duplicates is a common problem with sales lead data. A single field like email id can cause duplication of an entire record. For instance, different email ids – email@example.com, firstname.lastname@example.org, or email@example.com - reflect different records in the database. You must examine the entire database and adopt a right strategy to not let go any such instances of conceptual repetitions.
For the records of above type, go for merging. However, have a clear idea as to what values you would like to keep in the merged record. Relevance of field value and its importance in the marketing strategies are some factors that you must consider.
Records represent customer and prospects, so while you delete exact duplicates, review that the records you have selected for deletion are indeed duplicates, lest you end up losing crucial information. The best way to delete duplicates is to isolate duplicates, and then go for deletion.
You’ll find yourself at the advantage, if for data deduplication, you:
The genesis of accurate data is data entry. Whether it is incomplete records, non-standard values, and incorrect entries; it is data entry which accounts for fallacies as well success of your data-led decision making.
The importance of data entry in a typical leads database building demand that data entry should be standardized. A robust real-time check mechanism will ensure, in the very first step, that data is meeting all necessary prerequisites.
How this will work? Here are some examples:
Altogether, standardizing data at entry point is essential to facilitate correct real time sales lead data processing, and the implementation ensures data consistency.
Absence of data in source, failure of data entry operators in recording, unintentional deleting, amongst other related issues can result in missing values, causing incomplete records.
One great strategy is to collect lead data from multiple sources – websites, directories, and social media. There are specialized vendors that provide lead databases. You can tap into these options to perfect and enrich your lead database.
Fields have relationships amongst themselves, and you can leverage these relationships to infer certain values. They are particularly useful with fields like address. For instance, you can automate the process to generate city name, in case the city field has no value. Simple rules can be:
IF POSTCODE = P2, THEN CITY = C2
IF CITY = C1 AND LANDMARK = L1 THEN POSTCODE = P1
However, in real-life situations, rules are expected to be complex, and so before you attempt implementing the auto-generation process, validate the rules by subjecting them to different conditions. Incomplete validation of rules causes dirty data, and when dealing with contacts database containing millions of records, handling bad lead data would turn out to be a separate activity. So, have robust rules to have no dirty data.
Categorizing and finding best-fit leads is the mantra for conceptualizing successful campaigns and tailoring right communications. Segmentation allows you to understand where you need to give more focus and create excellent personalized campaigns for select audience.
You can segment the database by requisite field – occupation, designation, employment organization, industry, geography (city and state) etc.
Database segmentation is advantageous because you:
With B2B databases, lead data segmentation is a powerful means to converting MQLs into SQLs. By having your database divided on core competencies can allow your sales team to optimize action time. You will be more successful with cold calls, since campaigns will rightly hit the targets.
Segmenting data also improves the efficiency of support, as you can easily prioritize support requests. Knowing who your key customers are assists you align your support operations accordingly. This positively affects customer KPIs like customer lifetime value (CLTV).
Keeping non-interacting leads means you are unnecessarily incurring the cost of maintaining additional records and adding complexities to decision-making. One good action in lead database cleansing is to identify those leads which haven’t interacted with your brand for a certain time period (months or years).
You can identify inactive leads by checking which leads haven’t responded to your campaign emails for several instances, leads that haven’t shown any interest in filling out your forms, and leads who haven’t visited your webpages.
Similarly, there are leads that are consistently bouncing. These are the leads to whom you might have shoot multiple emails only to experience that they bounced for a given time period (quarter, half-year etc.). Correlate your campaign frequency with bounces and identify leads with higher proportion of bounces. When leads don’t show interest in your campaigns, they receive the mails as spams, and so the best action is to remove such leads.
If you have a good in-house team that you can dedicate to cleanse your lead data, then don’t forget that you leverage it effectively to stay ahead of competition. Along with this, don’t forget to consider every single factor that can lead dirty data enter your lead database.
But if you don’t have enough resources to create a robust data-based decision-making pipeline; forging a long-term strategic partnership with sales lead data cleansing companies will be a smart move. Banking on their industry expertise and experience, and strong delivery frameworks; helps you to boost lead engagement.