Common Challenges in Sales Data Management and Analysis

Managing sales data is a crucial task if you aim to maximize your performance and make data-driven decisions. However, this task is not without its challenges.

In this article, we’ll delve into how data quality, data integration, analysis, and staff training impact overall management.

Challenge #1: Data Quality

We know well that sales data can come from various sources (especially if you have a complex process with several actors in between), including CRMs, billing systems, marketing platforms, and more.

What usually happens? Inconsistencies are found in the data between different platforms, such as duplicate records and incomplete or incorrect data, leading to less accurate analyses.

According to Thomas Redman, the cost of poor data is typically between 15%-25% of a company’s total revenue. And in an era where artificial intelligence plays a prominent role, this can only worsen.

How to Solve Poor Data Quality?

By taking action after asking the following questions:

1. From which sources are we collecting data, and how do we ensure data integrity from the source?
2. Do we have standardized procedures for data entry?
3. How do we ensure data is consistently collected at all touchpoints?
4. What processes do we have in place to validate data accuracy?
5. Do we use automated tools to detect and correct data errors?
6. How often do we conduct data quality audits?
7. How do we manage the updating and deletion of obsolete data?
8. What policies do we have in place for data retention and archiving?
   

For the sales journey, the best approach is to understand where data entry points exist and ensure the collection, quality, validation, and correction in case of errors. The ultimate goal is for teams to trust the quality of the data they use daily.

Challenge #2: Data Integration

Once quality is addressed, the second problem arises: integration. We’re talking about data being generated from different platforms, each with a piece of information that contributes to the whole picture.

We have multiple data sources that are not connected, and we need to connect them. The lack of interoperability between platforms can lead to data silos, making it difficult to create a unified view of total sales performance (from when the user arrives to when they make a purchase).

How to Solve Data Integration?

Again, work after asking the following questions:

1. Do we have a clear and documented strategy for data integration?
2. What tools and processes will we use to integrate data from different systems and sources?
3. How do we handle data duplication and data conflicts between different systems?
4. How do we ensure data quality and consistency during and after integration?
5. Have we established a process for data normalization and standardization?
6. How will we monitor the performance of our data integration processes?
7. What metrics do we use to evaluate the success of our data integrations?
8. How do we ensure that end-users understand and trust the integrated data?
   

The goal here is to align all data sources to achieve what is known as a single source of truth (a place where everyone sees what’s happening, trusts it, and acts on that data).

Challenge #3: Predictive Analysis

Now, assuming we have good data quality and integration, we will be giving our Marketing and Sales teams good visibility of everything that has happened and what is happening almost in real-time.

But… what if we want to set goals for the whole year? What if we want to analyze how we’ll close our sales based on the current trend? For that, we need to predict. And this can be complex due to the need for advanced algorithms and large volumes of historical data.

How to Approach Predictive Analysis?

By asking the following questions:

1. What are the main objectives of our sales predictions?
2. What metrics are we using or going to use to measure the success of our sales predictions?
3. What historical sales data are we using or going to use for our predictions?
4. Are we considering or going to consider external data, such as market trends, economic data, or competitor data?
5. What prediction models are we using or planning to use (linear regression, decision trees, neural networks, etc.)?
6. How do we select the appropriate algorithms for our sales predictions?
7. How do we validate the accuracy of our prediction models? (CRUCIAL for teams to trust forecasts)
8. Do we conduct backtesting to compare our predictions with actual historical data?
9. How often do we update our prediction models?
10. Do we have a process in place to retrain models based on changes in data or the market?
11. What tools and technologies are we using for sales prediction?
12. Are these tools adequate to handle the volume and complexity of our data?
    

The idea of sales prediction is to have a relatively reliable process that the Marketing and Sales teams can use as a guide to understand whether they are performing well or poorly and what they need to correct or continue doing.

Challenge #4: Staff Training

We have good data quality, optimal integration, and predictions that more or less align with reality, but… only the Business Intelligence team understands them. We’ve hit a wall.

We want the Marketing and Sales teams to use and make the most of this data—how is it possible that they don’t know how to use or understand it? This absolutely limits our ability to be more data-driven. Moreover, these teams should be the ones who know best how to use this data since they have the most context and know-how.

Unfortunately, this is a classic challenge: cutting-edge technology in data management (in the best case) and very poor usage by the most important stakeholders.

How to Train Staff?

As with all the previous challenges, it’s worth asking some questions:

1. What are the critical competencies and skills our staff needs to perform their roles effectively?
2. How do we identify skills and knowledge gaps in our team?
3. What training methodologies do we use (workshops, e-learning, coaching, on-the-job training, etc.)?
4. Are the training programs aligned with the company’s strategic objectives?
5. Is the content of our training programs up-to-date and relevant to the staff’s current tasks?
6. How do we ensure the quality and accuracy of the training content?
7. How do we measure the effectiveness of our training programs (post-training evaluations, satisfaction surveys, knowledge tests, etc.)?
8. What metrics do we use to assess the impact of training on job performance (improvements in KPIs, productivity, work quality, etc.)?
9. Do we collect feedback from training participants to improve future programs?
10. Is the budget allocated for staff training sufficient to cover all identified needs?
11. Do we establish individual development plans for each employee that include training objectives?
    

The ultimate idea in this particular challenge is that the most important stakeholders in Marketing and Sales know how to use the data from their operations to correct what’s wrong and continue doing what’s right.

Conclusion

The management and use of sales data present multiple challenges, but with the right tools and strategies, it is possible to overcome these obstacles. Improving data quality, integrating different sources, leveraging predictive analysis, and training staff are fundamental steps to achieving this.