“It takes $1 to verify a record as it’s entered, $10 to cleanse and dedupe it, and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again.”
The 1-10-100 (and now 10,000) rule has become ubiquitous, raising awareness about the cost of bad data. There’s a bright side to this equation too though; where bad data costs millions, cleansing it can make millions. Forrester analysts claim that improving data quality to make it just 10% more accessible will add $65million to the bottom line.
In this blog, we’re going to focus on just one subject area where improving data quality can drastically increase ROI. You will find how cleansing your marketing database helps:
♦ Plug leaks in marketing so you can focus on the right leads
♦ Increase trust in lead data so you can create more targeted messaging
♦ Score leads more accurately so you can strategize better and close more
♦ Reduce email bounces so you can avoid the dreaded blacklist
♦ Prune your database so you can save on marketing automation fees
1. Reach the Right Prospects with the Right Message
Research reveals that 81% of the organizations surveyed consider customer insight a key priority, and yet, less than a fifth are able to use data effectively to optimize interactions with their customers.
So what’s stopping them?
♦ Variety of customer information
♦ Disparate sources of customer data
♦ Volume of available data
♦ Lack of data standardization
These are the primary factors that prevent organizations from getting a unified view of their customer or prospect data for use in creating personalized experiences and delivering the right message to the right prospect.
You can’t do that with bad data though.
Senior Forrester analyst Richard Joyce claims that 70% of marketers know they have inconsistent or poor quality customer/prospect data. To build a single customer view for increased marketing ROI, ensure that your contact data is cleansed properly and efficiently. Meaning, your data cleansing software of choice needs to be fast, accurate, and most of all, easy to use. It needs to be so intuitive that the people who know the data best, the business users, can profile, match, deduplicate, and cleanse their data themselves rather than submitting a request to IT and waiting days and weeks to get their data back. And even then, the way IT standardizes your data may not make sense on the business end, effectively reducing its analytical value.
“No more endless days cleaning data! I like that DataMatch Enterprise is very straightforward and easy to use. We had it up and running with very little training.”
Once you’re confident about the degree of cleanliness in your customer data, focus on making sure it’s up to date and complete. Record linkage features in your data cleansing software, powered by fuzzy matching algorithms, will help identify if additional records specific to the same customer entity exist across disparate sources. Merging these records will help create a “golden” record for each customer.
For marketers, obtaining clean and accurate golden records mean that they can now segment contacts better based on lead intent and prior engagement data, allowing them to reach contacts who are most likely to be engaged with the messaging they receive. The cleaner your contact data is, the more effectively you can personalize messaging and nurture leads to close more deals. Sending personalized emails alone can account for 58% of your revenue.
If you know your messaging is powerful yet fails to engage your leads and prospect, it’s time to spring-clean your contact data.
2. Assess Sales-Readiness with Better Lead Scoring
If you have a lead nurturing plan in place, you know how important it is to score leads accurately and reliably. Based on a lead’s behavior across all your different digital touchpoints (email opens, blogs read, ebooks downloaded, links clicked, etc.), they’re assigned a score for each activity. The higher the score, the more qualified the lead is — and once the score reaches a certain threshold, your sales team gets a “qualified” lead fresh out of the lead-nurturing oven!
Except: the score wasn’t properly calculated, the lead wasn’t really qualified, and sales spent an inordinate amount of time trying to sell to a dead-end contact.
Sounds familiar? At a Marketo User Group event, panelists unanimously pointed out bad and inconsistent data as the leading cause of problematic lead scoring and attribution. Demand Gen’s Lead Scoring Survey Report found that 86% of marketers leverage lead scoring, and yet, only 15% said that the leads that hit qualification thresholds were actually rated as qualified by their sales teams.
To reduce false positives, marketers need to prioritize data cleansing. This is most evident in the case of lead attribution. If you have 4 instances of the same customer entity in your marketing database, how do you decide where to increase scores when the customer interacts with your content? Focus on deduplicating and/or merging records so all interactions get attributed correctly, both negative and positive.
Another issue is data decay. B2B data decays at an alarming rate of 70.3% annually. If the information used to score leads is outdated or incomplete, the resulting score will automatically be misleading. Combine that with improperly entered, non-normalized, and poorly maintained data and you’re not only scoring leads incorrectly but also potentially missing out on numerous interactions that should be scored.
To develop an effective lead scoring model and assess sales-readiness accurately, start cleaning your data by:
♦ Standardizing the data you record and associated processes. Work with sales to identify demographics and behavioral traits for the ideal customer and decide on the information you will capture and in which format.
♦ Setting up real-time data hygiene checks in your data cleansing software to verify that data remains standardized, old data is cleaned up, and duplicates are purged and ideal records survived after merging important information.
Quick win: In your efforts to deduplicate records for better lead scoring and attribution, you will invariably end up pruning your database size. You know what that means, right? Reduction in marketing automation fees, because you get charged by the size of your marketing database!
3. Plug Leaks in Marketing with Data Cleansing Software
So far, we’ve talked about where and how you need to use data cleansing software to increase marketing ROI, but we haven’t yet focused on specific data cleansing practices so you can hit the ground running with your data quality initiatives.
Here are 4 best practices you can apply to your marketing data right now:
Profile Your Data
Start with profiling your data to get a quick overview of the kind of issues that exist in your data. Your data cleansing software should be able to point out incomplete data, spelling errors, inconsistencies, etc. You can get this done within a few minutes with DataMatch.
Standardize Your Data
Set some basic standards and use your platform of choice to enforce them. For instance, decide if you always want records to say “Street” instead of “St.”, or if you want company names to be entered in their full form or just acronyms. Create business rules that check for reasonable input range for fields like age (age can’t be less than zero or above 150). Disallow special characters and fields like customer name.
You get the idea. The more structured your data is and the lesser the variation in the input format, the higher the data quality.
Dedup Your Data
In most data cleansing software, you’ll be able to search for matches against one or more fields. If there’s a unique identifier like email address, your job is easy – just see which records contain the same value in the email field. Right? It’s not that easy. The same customer could have entered their work email one time, their personal email another, or used their Facebook account yet another time. Try different combinations to find the right matches.
Monitor Your Address Data
Both email and physical address data must be monitored, in terms of returned mail and email bounces. A high number of either metric automatically means you have dirty data. Some data cleansing software offers built-in address verification modules to help your correct this information on a timely possible, and/or complete any address information that is missing from your data in bulk.
Cleaner data has helped businesses save billions in marketing costs while increasing marketing ROI drastically. A veritable one-two punch, if you will. Data Ladder’s data cleansing and matching software is renowned for its accuracy and speed, outperforming giants like IBM and SAS in numerous independent studies.