Customer data provides value to the sales organization by providing insight into who is likely to buy, what products/services are being considered, how well salespeople are managing the process, and whether or not there are other growth opportunities. When this information is captured through a customer relationship management (CRM) platform like Salesforce, it significantly enhances the productivity of the sales force on a daily basis.
However, there are many common business problems that include duplicate contacts, incomplete lead information, outdated customer data, and inconsistent account information. Although some of these problems may appear minor in terms of their immediate effects, they can ultimately affect the ability of an organization to achieve its goals with respect to sales productivity, customer experience, pipeline visibility, and revenue forecasting.
An example would be a salesperson calling on a customer twice as a result of their business having two separate entries for the same person. A second salesperson could miss an opportunity to make a sale to a potential customer if they do not know what the customer wants, or do not have enough contact details for that customer. Lastly, sales managers can use reports that contain duplicate opportunities or incorrect customer data.
The above examples demonstrate why it is essential to collect a great deal of information within your CRM, yet this does not mean that the information you are collecting must be useful.
Salesforce Data Quality is important for companies to use when making selling decisions and continuing to build strong customer relationships. This article will focus on the most prevalent problems associated with data quality within Salesforce as well as the effects of having duplicate or incomplete records in Salesforce on the ability to sell. Let us explore several of the most typical Salesforce Data Quality Problems, the negative effects that those problems can create for sales, and what steps should be taken by companies to ensure that their Salesforce CRM data remains accurate.
Salesforce Data Quality is the accuracy, completeness, consistency, validity, uniqueness, and timeliness of Salesforce CRM data.
Access to high-quality CRM data will enable sales teams to get to know their customers, know what they want, know how they engage with the company, and know where they are in the process.
Poor data quality happens when records have missing or erroneous information, inconsistent data values, outdated data, or duplicate data about a person or organization.
Some of the most common Salesforce data quality issues are:
The issues usually arise with business data imported from two or more sources; external applications being integrated; and teams entering data differently.
If not addressed with a proper Salesforce data management solution, these problems can compound and threaten the credibility of the Salesforce system.
Wasted Time and lower sales productivity.
When there are duplicate records, sales reps will need to duplicate information, go through account histories, and figure out what is most current.
Employees do not need to be consumed by prospecting, follow-ups, and closing, but will have to conduct research and merge data from several records into one.
Let’s say that a company has this same person appearing three times in Salesforce, as they have downloaded a report, checked out a webinar, and filled out a contact form.
A single interaction, if it does not generate a unique lead in conjunction with other interactions that have been appropriately matched and consolidated, can be considered as three separate prospects and handled by the sales representatives separately.
This leads to wasted administrative efforts and reduced time for sales.
How to resolve this issue: Set up Salesforce duplicate management, set a [standard] process for creating records, and regularly audit duplicate accounts, contacts, and leads.
Duplicate customer records can create fragmented communication.
A single sales representative can submit a product offer to a prospective client, and a different sales representative can introduce themselves via email to the exact same client. Additionally, a prospective client that has provided his/her needs to a sales representative can have those needs re-stated to him/her due to the fact that the initial communication took place on a separate system.
The negative experiences above can create an image of disorganization for your company and diminish consumer confidence.
The issue gets larger when sales, marketing, and customer support teams use different copies of the same client data.
How to resolve this issue: Create a single source of truth for all customer data and make sure that only authorized personnel have access to previous communications with customers. Set up guidelines for each representative’s responsibility for managing specific opportunities or accounts.
Duplicate records can alter the perception of the sales pipeline as they create an illusion of more prospects, accounts, or opportunities.
A sales manager may receive a report showing that he has 500 leads and believes that he has many potential customers. However, if there are many duplicate leads within the report, then the actual number of “new” leads will be much lower.
Sales objectives, hiring decisions, lead assignment, and overall campaign results may be affected by this decision.
Duplicate opportunities can lead to inflated pipeline value, as a possible deal may be counted multiple times.
How to resolve this issue: Regularly audit your leads and opportunities, create consistent lead and opportunity reporting requirements for all of your sales reps, and ensure that you identify any duplicate opportunities prior to beginning your pipeline review.
The salesperson may not have enough information about the customer to understand their needs.
An existing customer account record and a new lead contact record for the same organization can exist. Without appropriate organization matching and relationship management, salespeople could potentially lose the opportunity to present products/services that are applicable.
Sales teams will be able to see the relationship they have with each customer, find out what each customer wants, and identify which customers may need to be expanded.
The information that will help you decide whether a prospect is a good match for your business and has high potential to make a purchase is adequate for qualifying leads.
The user might find it challenging to provide answers if they are missing information regarding their company (i.e., number of employees, what type of work the user does at the company, industry in which the company operates, business requirements for the company, and contact information).
The lead received from a tech company could be asking for more details about cloud consulting. The sales rep may struggle to understand who the prospect is (their company), what they need technically, and whether they can afford to purchase, if there isn’t an email address included in the lead data.
The salesperson may research the customer’s background prior to contacting him/her; however, the customer may also never be contacted.
To solve this problem: You should determine what the absolute minimum amount of information is required in order for you to consider a lead “qualified.” Then, you can use these same fields as part of the process of generating a new lead. Also, you should make your forms as complete as possible through the use of web forms, enrichment processes, and validation rules that are applied to your form. However, please remember that you do not want to create forms that are so complex that they are difficult for users to complete.
A sales team that has inefficiencies will not be able to contact and interact with prospects when it is best for the customer to do so because either the information about the prospect is incorrect or missing.
If you use an incorrect email address, your message may bounce back to you. An incorrect telephone number may prevent a follow-up call from being made. Additionally, if you don’t know which part of the order or previous conversation is relevant, you might receive communications that aren’t useful.
Therefore, those leads that demonstrate potential might have fewer relevant opportunities for interaction with your company and might not move down your sales process at the speed you would like.
To solve this problem: Create a process to verify all contact information; document each major meeting; update records based upon those meetings. Create a report to identify missing or expired information and obtain the required information.
Lead Scoring is used by many Sales Teams so that they can rank Prospects based on criteria such as: Prospect Company Profile, Prospect Engagement, Prospect Role, Prospect Buying Intent.
The accuracy of your score can be affected when critical information is missing or incorrect.
For example, a prospective customer that has high potential value to the organization may have a low score if the “Company Size” field is empty. Additionally, a non-qualified prospect could seem more appealing to the organization because of out-of-date or incorrect information matching your scoring criteria.
The representative may lose out on quality opportunities by spending too much time with lower-quality leads.
To solve this problem: Review the fields being utilized for lead scoring and develop minimum quality standards for those fields. Additionally, where applicable, provide an explicit method for dealing with missing data instead of simply assuming that a lack of data indicates that the prospect has no interest in purchasing from your company.
Salesforce data quality issues go beyond just single-record issues. There are multiple areas where poor data quality can negatively impact the overall sales organization.
Unreliability of Revenue Forecasting: Duplicate Opportunities, Inaccurate Deal Value, and Out-of-Date Sales Stage reduce the Reliability of Pipeline Forecasts.
Higher cost for acquiring customers: Sales representatives are using additional resources to produce similar levels of success when they use their time to research prospects that will not purchase from them.
Ineffective marketing campaigns: The effectiveness of marketing campaigns will be impacted by ineffective segmentation, inaccurate campaign reporting, and irrelevant marketing communications due to duplicate contact records and incomplete customer profiles.
Poor management decisions: When making resource allocation choices, business leaders can base their decisions on inaccurate reporting of sales performance and/or on faulty assumptions regarding sales performance.
Reduced employee use of the CRM system: Employee usage of the CRM system will decrease when employees are continually exposed to incorrect or contradictory information. They may lose faith in Salesforce, and they will likely start to maintain their own spreadsheets or databases.
The cost of an organization’s financial loss due to poor data quality will depend upon its size and scope; however, the cause for this problem is universal: poor data quality leads to inconsistencies within an organization’s operations.
The process of improving Salesforce data quality goes beyond simply removing duplicate records. To achieve long-term improvement in data quality, businesses must establish preventative controls, monitor their data regularly, and define clear responsibility for maintaining accurate customer information.
Salesforce uses both matching rules and duplicate rules to assist with identifying and managing possible duplicate accounts, contacts, and leads.
Matching rules specify how Salesforce compares records. Duplicate rules specify what will happen once a potential duplicate record has been found. Depending upon how the rule was configured, users may be notified, records may be restricted from being created/edited, or potential duplicate records can be submitted for review.
For example, a business would set up matching rules (matching criteria) by using their customers’ email addresses, company names, and phone numbers in order to determine whether they had any duplicate leads.
The rules must match the way that the organization recognizes its customers. Rules that have too much breadth in their matching criteria could cause false positives by flagging legitimate records of other individuals or companies.
Additionally, Salesforce has resources to review duplicate records and combine relevant ones.
The use of validation rules is designed to ensure that users are unable to store records that fail to meet specific business requirements.
For example, if a business may need to find the name of the organization that generated the lead (for lead generation purposes) or set criteria for when a deal can progress to another part of the sales cycle.
The controls can decrease the number of records that are incomplete, as well as make the information contained in the CRM more consistent.
Validation is an important part of creating a lead in Salesforce. However, if you require employees to fill out every available field when they enter a new lead into Salesforce, it could prevent them from creating the lead, or at least slow down the process.
Concentrate on the most critical information during all steps of the sales cycle, then add more details as the account moves forward.
Different employees may enter the same information in different ways.
One of your representatives might use “ABC Technologies Pvt. Ltd.” when recording a company’s name, while a different representative uses “ABC Tech” or “ABC Technologies.” You may find similar inconsistencies in how phone numbers, locations, industries, and job titles are recorded.
Standardized data entry helps to reduce the variability of the data and increases the accuracy of your reports.
Businesses can define picklists for relevant fields, establish standard naming conventions, document data entry guidelines, and train their sales forces on these standards.
As with any software, even the best customer relationship management system will have some of its records go out of date or contain errors as it ages.
An ongoing series of regular audits of your company’s sales force data will provide you with the opportunity to find missing fields, identify duplicate records, locate inaccurate contact information, and determine where there are discrepancies in your company’s sales force data that may have an impact upon your company’s reports.
Administrators may be able to find records which require attention through the use of Salesforce reporting, duplicate jobs, and/or other applicable data quality applications.
An effective audit could evaluate what percentage of leads do not contain an email address, how many accounts have the potential for duplication, and what percentage of opportunities were not updated in a specific amount of time.
Assigning accountability for addressing those concerns rather than just producing reports and then leaving the results without resolution is what businesses should do.
Salesforce receives data from various sources such as marketing automation platforms, online forms, enterprise resource planning (ERP) systems, spreadsheets, and other external applications.
Poorly designed data flows that are part of an integrated system will either result in duplicate entries, overwriting valid field values, or producing conflicting records.
Before adding a new source, identify how records from the two sources will be matched up, who has ownership of all of the data in each field of the record, and how to handle conflicting updates.
Use relevant id’s, map fields, validate, and monitor errors. Run tests on the integration using realistic data prior to implementing throughout the company.
Data quality improves when businesses measure it consistently.
Useful Salesforce data quality metrics include:
The Lead Conversion Rate: It refers to the percentage of leads that move forward in the sales process to the next level. To assess how well your lead generation efforts are working, you will want to evaluate this conversion rate based upon where the lead came from (source) and the quality of the lead itself.
Companies will be able to identify repeat issues and determine whether their data quality program is working effectively using these metrics.
The sales results of a business can be affected by several variables other than the quality of the data it has available. These variables include but are not limited to: How the leads were generated; What type of sales methodology is being employed; The price structure that is being used; whether the product being sold applies to the target audience; And, current economic conditions. All of these variables will contribute to whether or not a visitor becomes a customer.
Imagine a business-to-business (B2B) software company that acquires leads via its website, LinkedIn marketing, and attending industry trade shows.
As time goes on, the sales department finds that some of the same prospects are listed several times within Salesforce. In addition, some records do not have complete information regarding a company, and some other records include outdated contact information. Finally, sales management is finding that there are discrepancies between the lead reports they receive and what the prospects actually do.
The first step for the company is to perform an audit on its Salesforce database. This will help them find duplicate leads, incomplete records, and out-of-date information.
The system will then configure matching and duplicate rules, validate the key fields on leads, and provide consistency in the data related to companies and industries.
In addition to defining ownership of every account, the team has developed processes for updating contact information and reporting on any incomplete or outdated records.
Following the implementation of these changes, the sales force will have access to more accurate customer data, while management will have a clearer picture of lead quality and sales pipeline activity.
Improvement occurs through the integration of multiple tools (Salesforce functionalities) with clearly defined processes, individual accountability for performance, and continuous oversight.
When salespeople don’t have accurate data in Salesforce, they may lose productivity as a result. They will also find that their relationships with customers are damaged. In addition, the reports they produce on their pipelines may be distorted. Finally, when salespeople try to prioritize leads based on data in Salesforce, their decisions may be less reliable. Duplicate records can create confusion and extra work for salespeople. Records that are incomplete do not allow salespeople to understand and interact with potential customers in the most effective manner possible.
Businesses can resolve these issues through the use of Salesforce duplicate management, configuration of validation rules, standardizing data entry, performing routine audits of data, and tracking significant metrics for data quality.
The problem of keeping your CRM data accurate and relevant will always be there. There are many ways that your data could become out-of-date or inaccurate. You may add new records, integrate with other systems, customers change their contact information, or the way you do business changes.
Organizations that create a well-defined process for who owns what type of data and integrate the idea of “data quality” into their daily sales activities have a higher chance of successfully using Salesforce.
The bottom line is that improved Salesforce data quality allows your sales team to spend less time on record cleanup and more time developing relationships, managing opportunities, and completing deals.
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