5 Signs you Need to Improve the Quality of Your Sales Data

5 Signs you Need to Improve the Quality of Your Sales Data


Accurate sales data is the key to effective forecasting, customer relationship management, and competitive analysis. Yet, many businesses struggle with the quality of their data.

If you’re wondering how you can tell if it’s time to reexamine your data collection methods and check you’re spending resources on reliable, accurate, and trustworthy data, here are five clues that will help you know for sure.

1. Your salespeople approach sales interactions with a lack of knowledge

Quality sales data reveals information about your consumers and aids in the creation of efficient sales workflows that increase conversions. You must seek out meaningful, prescriptive insights into what motivates these leads to convert and return so that you can build on what is working and tweak what isn’t.

If you find that the majority of your sales discussions take place in the dark, with no prior understanding of the lead’s pain points and preferences, your sales data is insufficient.

What can you do about it?

Make sure to ask questions that delve into your leads’ pain spots and wants, but also broaden your lead research process to include social media analysis and persona interviews to garner additional relevant customer data.

You can also acquire a verified sales contact list along with all relevant information, such as roles, experience, phone numbers, etc. Utilizing a pre-vetted B2B database can help cut the work for your sales team while still ensuring they have all the relevant customer information.

2. You make decisions based on emotions rather than facts

Too often, sales teams claim to be data-driven, but they’re really driven by emotion. They gather data that fits their agenda rather than objective data that defines the agenda. Pre-programmed prejudices can infiltrate and alter the data you collect in a variety of ways, and if you suspect that your data is of low quality, you’ll find it simple to skew it to produce only the answers you want.

What can you do about it?

The first step in preventing bias from infiltrating your data collection processes is to be open about your data quality issues and the likelihood of prejudice. You must train salespeople to understand not only what data to collect, but also how important proper data collection is. They are more likely to acquire data that is relevant and comprehensive if they understand why the data they collect is important and how it will be used.

3. You’re surprised by your current customers

Unfortunately, the need to seek new customers can lead many sales staff astray. They fail to establish data gathering channels for existing consumers, resulting in sparse datasets and a general lack of knowledge about what loyal customers like. The unfortunate reality is that if you don’t understand your consumers’ demands and don’t bother to enquire about them, you won’t be able to keep them. Check to see how many data collection channels you’ve set up to collect data from existing clients on a regular basis.

What can you do about it?

It’s critical to check existing client data on a frequent basis to ensure that it’s accurate. You should also review your data entry process in order to build standardized data input and update procedures that will increase the quality of your data. Make sure to opt for reliable inside sales software that allows you to ensure proper data collection and offers note-taking capabilities. That way, you can ensure that all relevant information is recorded about each customer.

4. You have no idea whether your results are excellent, poor, or ordinary

Sales statistics will only get you so far. Even the most objective and meticulously collected datasets are useless if you don’t know how to evaluate them. Set benchmarks to measure metrics and analyze data if you want to gain relevant insights.

What can you do about it?

Make sure you’re measuring the incorrect metrics by tying your KPIs to the organization’s bottom line. You’re probably measuring the incorrect things if your existing data structure doesn’t provide explanations for specific situations. Focus on establishing peer group benchmarks through market research, grading your results on a percentile basis, and developing strategies to enhance your outcomes.

5. New leads are available in a variety of demographics

When it comes to acquiring consumer data, you need relevant data that allows you to efficiently filter new leads into current categories. 

Some of the most typical errors are providing too many alternatives, adding too many fields, and being inconsistent regarding mandatory fields. However, it isn’t always true that more is better.

Users can also be overwhelmed by sentiment scales with too many alternatives, such as one-to-ten scales for customer satisfaction inquiries. Someone can give you a five because they were ambivalent about your goods, while someone else might give you a six because they despise it.

What can you do about it?

To maintain data quality, standardize your data gathering techniques and limit fields and options to a minimum so that leads aren’t confused or overwhelmed. To make it easier to distinguish groups and develop targeted campaigns, it’s recommended to include three to four options for each question.

Over to you

Sales data is critical for keeping your company competitive and driving revenue, yet many teams fail due to poor data quality. If your sales organizations are consistently surprised by current clients, struggle to segment leads and track progress, approach sales calls blindly, and frequently find that data verify their original assumptions, something is likely wrong with your data quality.