The negative consequences of poor data quality include low inbox placement rates, flawed sales forecasts, decreased client retention rates, and decreased revenue. It may also put your company at risk of violating data privacy laws around the world such as GDPR, CASL, CCPA, and others.
When trying to improve your data quality, look for these common operational weak points associated with the data collection and data management process.
- Multiple data sources: Lead generation comes from trade shows, conferences, cold calls, partner referrals, a company website form, a website chat, inbound email, social networks, and others. With so many data collection points, the manner in which data is gathered and entered into the system is often inconsistent and unreliable.
- Compare all data collection points and processes to identify gaps in required fields, naming conventions, contact permission, expectation setting, and lead qualification.
- Siloed departments: Communication between marketing, sales, and data processing as to what the standards are for acquiring and entering data into a system can always improve.
- Meet with representatives from all affected departments periodically to establish and reinforce data standards.
- Department discussions usually uncover additional pain points and operational gaps specific to your business or industry that may impact data quality.
- Too much focus on lead quantity over lead quality: Some B2B companies cut corners when acquiring prospect email addresses and employ high risk methods such as buying lists and scraping email addresses from social networks such as LinkedIn. These high risk data collection methods often contain outdated and incorrect information, which cause deliverability problems and result in a high number of unqualified leads.
- Don't buy lists or scrape email addresses from social networks because they are not qualified leads. The more qualified the lead, the better chance you have at making a sale.
- Inadequate information: Often times, only a prospect's name and email address is collected. There is no indication of who they are, what their pain points and needs are, and if they are actively looking for a solution to their problem (if they know a problem exists).
- In addition to getting an email address, find out their role and if they are an influencer or decision maker. Determine their stage in the sales funnel to help shape your content strategy.
- Obtaining additional information may require adjustments to online forms, call scripts, and discovery questions.
- Review data requirements with sales and marketing to ensure the most relevant data is collected.
- Inadequate data validation: People make mistakes when writing down or entering information into a computer. They also abandon email addresses and change jobs. These actions may result in malformed domain names, misspelled addresses, unknown users, role email accounts (sales@, marketing@), incorrect email syntax, and duplicate data. Incorrect data means a missed opportunity.
- Implement data quality checks with people and technology on an ongoing basis to improve data collection and data hygiene.
- Inadequate email bounce processing: A receiving email server notifies the sending server when the intended recipient is unknown or invalid through error or bounce codes. Upon receiving an unknown or invalid bounce code, that email address should not be sent email in the future.
- Review the list of email addresses labeled as unknown or invalid and update your CRM and other contact lists.
- Take steps as required by your business relationship to obtain updated contact information. You may need to contact other people within their organization or prompt users to update information at their next login.
- End-user adoption: A high administrative burden is a barrier to user adoption. It takes time away from your sales representatives talking to prospects and customers. If system usability and data entry requirements are too cumbersome and lengthy, many sales representatives enter the minimum data allowed in order to move the deal to the next stage. Repetitive, manual tasks are also major pain point and lead to mistakes or incomplete data.
- Implement automation to improve data management efficiencies.
- New employee onboarding: New employees don't know your data management practices and are sometimes overwhelmed with information during their first few weeks or months of employment.
- Be sure to review and reinforce data management policies as needed for their role during the first ninety days of employment.
