Data Quality vs Sales Reps: A Guide to Relationships Building

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Data Quality vs Sales Reps: A Guide to Relationships Building

The manual entry is still an essential approach to collecting data despite the automated information capture. The sales reps put all the necessary records in CRM received during the communication and other interactions with potential customers. The challenge is the sales managers neglect entering new and relevant information into the platform. Instead, they want to keep selling. As a result, data is usually put in sparingly or incorrectly, and in the worst scenario not entered at all.

Even CRM solutions with compelling algorithms can fail without the unified data quality rules.  The absence of data management strategy can spur the following challenges:

  • failures and delays in service providing
  • inaccurate or redundant contact data
  • inappropriate product offers due to ineffective client segmentation
  • additional expenses on incorrect invoicing and overpayment
  • gaps in security that may cause sending personal data to wrong addresses
  • misinformed strategic decisions
  • inaccurate revenue forecasting
  • inefficient campaigns across different channels

Moreover, there is some concerns from managers on the improvement of data entry by sales reps. Read on to learn why data quality is so critical, and how to get your team on the track to consistently inputting precise data.

5 Vital Points in Data Entry Rules Setting Up

Maintaining strict and reliable information entry policies are the keys to assuring your records are both accurate, as well as consistent. Due to the reason the information quality should be an integral part of the sales coaching, let’s go over the main 5 strategies that can help you get the reps to care about quality data.  

#1 Train your sales team

Driving sufficient data input practices starts during onboarding procedure. Instilling values of records hygiene in the beginning goes a long way in the further adoption. Enforcing or adding data rules can become more challenging if your sales team get comfortable with the process as it is.

In this case, you can organize morning meetings run through each rep goals for a day. Then, you can set up weekly one-on-one sessions with the reps to go over their individual performance working with your CRM data. By spending a lot of time using data analysis in such kind of meetings, you can emphasize the value of accurate data entry.

#2 Limit the options

This strategy spurs the importance for the sales reps who are entering data as well as the ones reporting on it. Teach your employees to avoid the text fields as much as possible to simplify the process of data entry. Show them how to use checkboxes and drop down menus. This also makes the analysis easier as you don’t have to decipher what was hastily typed into the text boxes.

#3 Fix up field requirements at different stages

This complex practice requires the balancing between various opinions. On one hand, the management and marketing teams want to have as many entered fields as possible. On the other, the sales reps that are not willing to spend their time on populating the CRM items at every stage. Applying the right equilibrium requires cross-department participation to assure everybody informed with rules.

#4 Integrate with other software

Today many businesses use a set of different solutions, so it is worth to integrate your CRM with other software to eliminate the possibility of repeated data entry. For example: by integrating email or phone applications with your CRM, you can automatically capture these actions to save time for your reps to input this data.

#5 Show the value

Educate your sales team of proper information input. By showing employees the benefits they will gain from improved data entry, you receive the best chance to accelerate adhering the policies set into place.

Closing Thoughts

Being designed to facilitate data management, CRM platforms enhance the sales managers' performance as well. So, the critical point in success with CRM is the amount and quality of records. Bad data quality inevitably leads to inaccurate revenue forecasting, poor customer service, and misinformed strategic decisions. Consider all the tips and tricks above and create your own rules to foster quality information maintenance.

P.S. Сheck out the white paper “Explore the Essence of Quality Records for Your Productivity and Income Growth”, and find out how to succeed with quality records, organization productivity, and income growth.

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