5 Signs It’s Time to Improve the Quality of Your Sales Data


Businesses have been worshipping big data for some years already, but unforeseen events in 2020 and 2021 made it clear that revenue rests on sales data.

The pandemic turned markets and sales expectations upside down, leaving only 27% of CEOs feeling confident they’ll see revenue growth in the next 12 months. Business buyers are operating on tight budgets, and 36% of consumers intend to cut their spending this year, compared to 19% who gave the same answer at the beginning of 2020.

Attracting new customers is tough, and even loyal shoppers can’t be taken for granted. McKinsey found that 75% of consumers tried a new brand or shopping behavior in the past year, and another study reported that post-COVID loyalties are more fragile than ever. Fifty percent of new users acquired during 2020 had already churned by the end of Q2 2020, and the average retention rate is 82% lower than expected.

Consumers expect personalized experiences, lightspeed response times, and purchase journeys that remain connected no matter how many channels they cross. The only way to meet these expectations is to up your sales data game. For most companies, this isn’t news. That’s why e-commerce professionals have set data and analytics as their top budget item this year. The average enterprise is already drowning in data; what they’re missing is quality data.

It’s time to improve the quality of your sales data

Salesforce’s State of Sales 2020 report reveals that most sales teams will focus on improving data quality and accessibility in the next 12 months. 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 to know for sure.

1. Your employees approach sales conversations in ignorance

Quality sales data tells you about your customers and helps you build effective sales workflows that drive more conversions. That means more than simple results like “25% of leads aged 25–34 converted.” You need to look for useful, prescriptive insights that tell you what motivates these leads to convert and return so that you can double down on what works and adjust what’s not succeeding.

If you find that most sales conversations take place in the dark, without enough prior knowledge about the lead’s pain points and preferences, that’s a sign your sales data is insufficient. Sales conversations should build on existing customer knowledge, not lay the foundations of it.

How to solve it

Make sure to ask questions that dig into your leads’ pain points and needs, but also expand your lead exploration process to glean more relevant customer information from tracking pixels and social media analysis.

It’s also crucial to make sure that your data collection practices are fully streamlined and user-friendly. Your marketing and sales teams might be gathering all the data you need, but if your sales employees can’t access it at the right time, it might just as well not exist. You need to make it as close to effortless as possible for your sales representatives to record customer insights in your CRM and for others to find them again when needed.

2. You rely on feelings rather than data

All too often, sales teams claim to be data-led, but they’re actually emotion-led. They collect the data that matches their agenda instead of objective information that sets the agenda. There are many ways that preset biases can creep in and affect the data you collect, and when you sense that you have poor quality data, you’ll find it easy to skew it to deliver only the “answers” you’re looking for.

Some of the ways that enterprises bias their data collection include:

  • Surveying a small sample size that can’t provide a clear snapshot of the entire market
  • Ignoring seasonal fluctuations, like spending patterns that increase in the runup to the winter holidays and then drop in mid-January
  • Neglecting to include key customer personas in your sample audience
  • Adding irrelevant questions that could tempt participants to misrepresent their true reactions

How to solve it

The first key step to stop bias from invading your data collection methods is to be honest about your lack of data quality and the possibility of bias. You can use the calculator at Qualtrics to determine the sample sizes you need to draw reasonable conclusions. It’s also a good idea to ask external parties to review your survey questions for subconscious biases.  You need to coach sales employees to understand not just what data to gather, but also the significance of the data. Once they understand why the data they collect matters and how it’s going to be used, they are likely to take more care in gathering data that is relevant and comprehensive.

3. You don’t know whether your results are good, bad, or average

Sales data alone can only get you so far. Even the most objective and carefully-collected datasets aren’t going to be of much use if you have no way to assess them. If you want to derive meaningful insights, you need to set benchmarks to track metrics and interpret data.

How to solve it

Ideally, you need both internal and external benchmarks. Internal benchmarks allow you to compare performance against previous marketing campaigns or sales drives, while external benchmarks enable you to see if your open rates, CTRs, and other metrics are objectively good, or just better than they used to be.

Tie your metrics to the organization’s bottom line and consider whether you’re measuring the wrong metrics inadvertently. If your current data framework doesn’t offer explanations for certain events, you’re probably measuring the wrong things.  Focus on creating peer group benchmarks through market research firms and trade associations, grade your results on a percentile scale, and create processes that will improve your results.

4. Your existing customers surprise you

Good quality sales data acts as a window into customer behavior so that you can confidently and accurately forecast what your current customers will do and desire. Although it’s impossible to predict the market’s every move, you should have an overall grasp of their preferences and changing demands.

Unfortunately, many sales departments get carried away by the drive to acquire new customers. They forget to set up data collection channels for current customers and end up with sparse datasets and general ignorance about what loyal buyers prefer.  The sad truth is that you can’t expect to hold on to your customers if you don’t understand their needs and don’t bother to inquire into them. Check regularly to see how many data collection channels you’ve put in place to gather existing customers’ data.

How to solve it

It’s important to regularly review data about existing customers to see if it’s accurate. If your hard bounce rate is rising, it’s a neon light that existing customer data needs a review. Look to see whether you have the right contact details, and ask about their preferences and interests on a regular basis.

You should also re-examine your data entry process to create standardized data entry and update procedures that will improve your data quality. A common reason for incomplete data is the lack of consensus on required fields, for example.

The rise of auto-form filling has also been a double-edged sword. You might be sending out all the surveys you need on a regular basis to keep on top of your customers’ needs, but your customers aren’t paying attention as they fill them out. They leave it to autofill, which may or may not be up to date. It can help to include an open-ended question that leads can’t just leave to be filled in automatically, to force them to reflect and share their own opinions. Additionally, you need to create valuable, educational content that molds your target audience in the right ways.

5. New leads fit into multiple demographics

When you’re capturing consumer data, you need relevant information that enables you to segment new leads effectively into existing categories. More isn’t always better.  Common mistakes include:

  • Offering too many options, like including both “insurance” and “financial services” among your industry fields. Users might be unsure which to choose, so you’ll end up with leads straggling across segmentation lines.
  • Being inconsistent about obligatory fields, like requiring first names but not last names, or job title but not vertical.
  • Creating too many fields. When forms are too long, users are more likely to skip fields and leave holes in your datasets.

Users can also get confused by sentiment scales that have too many options, like questions about customer satisfaction that range from one to 10. One person might mark five because they felt neutral about your product while someone else chooses six to show they really disliked it.

How to solve it

It’s important to standardize your data collection procedures to preserve data quality and keep fields and options to a minimum so that leads don’t get either confused or overwhelmed. It’s best to offer three to four options for any question to make it easier to discern segments and prepare personalized campaigns.

However, you should move beyond the basic name-email-phone number triad on forms, and include one or two more in-depth questions. This is especially relevant when you’re targeting top or middle funnel leads who aren’t likely to be ready for a phone call.

When you have richer information like industry type, job level, or current provider, you can segment leads with greater precision and deliver more meaningful content to nurture them further.

Quality data is the foundation of effective sales

Sales data is crucial to keep your enterprise competitive and drive revenue, but many teams fail simply due to the quality of their data. If you find that your sales departments are repeatedly taken by surprise by existing customers, struggle to successfully segment leads and track progress, approach sales calls blindly, and repeatedly find that data confirms their original assumptions, it’s likely that something’s gone wrong with your data quality.

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