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Avoiding Bad Data in the Salesforce Sales Cloud
A lot has been said about Salesforce capabilities. 90% of all Fortune 500 companies use this CRM software to manage business relationships. That said, not everyone may be using this tool to its full potential. The prime reason – questionable data quality.
It is only when your data meets high-quality standards and is verified to be current, accurate, complete and unique that the insights drawn from analysing it can be beneficial to the organisation. When you have access to good quality data, you can forecast customer demand accurately, identify opportunities to make sales, make informed decisions about marketing strategies and deliver personalised service that earns your customer’s loyalty.
Let’s look into how you can avoid bad data in the Salesforce sales cloud.
Salesforce Sales Cloud Structure
Before we get into data quality improvement measures, it is important to understand some of the key objects in the Salesforce sales cloud structure.
Leads can enter the database through marketing channels or be created manually. This is where your sales funnel starts. If data is inaccurate at this stage, it will affect all the funnel stages that come ahead.
When leads are converted to qualified, they become contacts. Duplicity is one of the biggest data quality issues with Contacts. Having multiple contacts linked to an account makes it difficult for the sales department to offer personalised service. This can also affect the efficacy of marketing campaigns.
When leads are qualified, the company they are linked to becomes an account. Accounts can have multiple contacts linked to them. Hence, duplication of records is a big data quality concern here. At the same time, an account must have at least 1 contact linked to it. Hence, missing data can also be a data quality issue.
A deal in progress is referred to as an opportunity. Opportunities must be linked to an account and are managed through varying stages of maturity. Data quality is important for opportunities since reporting data is pulled from here.
Salesforce data quality challenges
Data quality issues can be the result of many different factors. The top 3 amongst them are:
Poor data governance
Data governance refers to the rules and processes followed by an organisation to enter and handle data. Proper governance is required to minimise data quality issues at the entry stage and to keep it from being outdated in the database. For example, manually entering customer addresses can increase the risk of typographic errors while using an address autocomplete service minimises this risk and ensures that all details are captured accurately.
Poorly trained sales team
It is not only the IT department that should be concerned with data quality. The people entering and interacting with the database must also be trained on how to input and update information. A lack of training can cause serious data quality issues. Users may create duplicate records instead of updating an existing one or delete information by mistake.
Lack of effort
Sometimes, it is just a lack of effort and not understanding the criticality of maintaining good data quality. Organisations that don’t try towards checking data quality and improving on it will find small issues quickly snowball.
Improving Salesforce Cloud Data
According to estimates, 5% of any organisation’s Salesforce data is inaccurate at any given point of time. Small steps go a long way in improving this data quality. Some of the things you can do are:
Check all incoming data
Irrespective of the source, all data entering the cloud must be checked against all data quality rules. Let’s take an address for example. It must have an apartment number, building name, street name, city name, district and pin code. All of the above details must be verified to check that the address exists and is deliverable. Above all, it must be linked to the relevant account name and it must be unique.
Checking data at this stage can keep bad data out of your system and help improve the overall quality of data considerably. It also helps you assess data sources. If you are repeatedly getting poor-quality data from one source, you may need to find an alternate source.
Perform data health check assessments!
Checking your data from time to time gives you the opportunity to discover anomalies before they affect your decisions and operations. It also helps spot patterns and trends that could indicate the source of the issue. For example, data coming in from one department may not comply with the format being followed by other departments. This process should ideally be automated to maximise efficiency.
Update data regularly
You also need to make an effort to regularly update your Salesforce data. The relationships between a brand and prospects/ leads/ customers are constantly changing. As far as possible, records should reflect the current state of customer relationships. For example, if a lead has been guided through the sales funnel and he’s made a purchase, your records should reflect the same.
Select the Right Data Quality Tools
Manually checking thousands of records to verify quality is next to impossible. You need to integrate a data verification tool with your Salesforce database to automate the process. There are numerous tools available in the market. To find the best tool for your organisation, make sure it can be integrated directly with Salesforce and can verify your data against reliable, third-party databases. It should ideally be able to address all data quality issues and not just highlight existing issues but take measures to correct and enrich the data. For example, if an address is flagged with an outdated street name or a missing pin code, the tool chosen should be able to correct the street name to its current name and add the pin code.
Finding the right data quality tool can increase trust in your Salesforce cloud and help save money in the long run. Remember, issues that are ignored never go away, they only grow bigger. So, take action now.