If you were planning on an epic cross-country road trip, you wouldn’t want to bring a car that’s held together with duct tape and a dream, liable to break down if the road looks at it wrong. You need to count on it to get you from point A to point B safely and without […]
If you were planning on an epic cross-country road trip, you wouldn’t want to bring a car that’s held together with duct tape and a dream, liable to break down if the road looks at it wrong. You need to count on it to get you from point A to point B safely and without incident.
Maybe you’ve never heard that data is like a car. But hear us out: the data insights you depend on should be as reliable as a Honda. Especially since you rely on those insights to take your business to new heights. If you’re gonna strap yourself in for a long ride, you need to know you can trust the data insights driving your decisions.
In an ideal world, your data insights will bring clarity to a business issue, helping you solve the issue and plan for better decisions moving forward. But you have to drive with the right insights; if you don’t watch out, you can hit some major roadblocks in your sales and marketing strategies.
What makes an insight unreliable? Is it when your data is inaccurate or disorganized? Not having enough data? Bad data is only one part of the equation. In fact, one of the biggest issues is good old-fashioned human error. Your data insights are only reliable if your team knows what they are and how to work with them.
So how do you make sure your data insights won’t cause you to crash and burn? Consider this your data Driver’s Ed.
We’ll explain what a data insight is, how to spot the red flags that indicate you may be approaching the data incorrectly, and how to turn it all around so you have actionable insights.
What are data insights?
Data insights are the understanding an individual or team gains from analyzing and interpreting data. This can be the data surrounding a particular customer base, segment, campaign, marketing channel, touchpoint, or team performance. Look for patterns and relationships among data points to form the insights that help you make strategic decisions.
What’s the difference between data and data insights?
Data is the information you’ve collected– just cold facts and figures like demographic information, behavior, or activity.
The data insights are the value and knowledge you gain after analyzing the raw data. They are the informed conclusions you draw from finding patterns and relationships in your data.
Think of data points as individual pixels, and the insights as the whole image that becomes clear when you zoom out and see all the pixels together.
Data insights lead to better informed decisions about budget, time, customers, and leadership choices.
Why you should care about data insights
Data insights save you from making big decisions blindly. Intuition and trial and error have their place, but neither is a strategy that results in sustainable growth. Data-informed decisions are the key to moving your sales and marketing forward with confidence.
A company that’s focused on growth and improvement doesn’t make space for guesswork. It uses data insights to do things like:
- Predict customer buying trends by monitoring current behavior patterns and customer complaints and anticipating what products, services, or updates they’ll need in the future.
- Increase customer retention by looking at why customers don’t make a purchase, when a customer usually churns, and using intelligent insights to learn how to prevent it.
- Improve its sales team’s performance by seeing how prospects and customers respond to certain sales tactics and understanding what strategies close the deal best.
- Create hyper-personalized marketing campaigns by gathering as much firmographic (company), psychographic, and geographic data as possible on its customers and crafting messaging around that information.
Data insight examples
Now you know what data insights are, why they’re important, and what they can do for your organization, you probably want to know what they might look like. Let’s take a look at a few examples of valuable data insights:
- Data: Customers complain that sales reps often take over 72 hours to respond to their messages.
Data insight: You decide your reps should receive training on how to automate and improve response times. - Data: The three days following a weekend sale show the highest sales for your product all year.
Data insight: You conclude that next year, more of your marketing and advertising budget should be allocated to the sale to capitalize on its popularity. - Data: Customer segment A opens your email marketing sequences 10% more than customer segment C.
Data Insight: You conclude you should tweak your strategy and try new email subject headlines for segment C to increase the open rate. - Data: You get 15% more business referrals from long-term customers after you send a personalized gift to decision-makers.
Data insight: You develop a gifting strategy for long-term customers to get more referrals.
7 ways to convert data into actionable insights (and 7 red flags you need to adjust your strategy)
Focus on key business drivers
If you don’t know where to start with data insights, try and focus on key drivers of business success like cash, profit, assets, growth, and people. By starting with this focus, you can leverage insights where your business will be most impacted and get the best ROI.
Questions to ask as you look at your data include:
- What are our key drivers of revenue?
- Where can we cut expenses to keep more cash on hand?
- Which marketing and sales tactics receive the highest conversion rates, and which the lowest?
- How can we train our reps to sell in a way that resonates with customers?
- What technology might equip sales reps to sell better?
Red flag: If you have difficulty gathering useful insights from your data, you may need to ask different or more specific questions. Take another look at what you need to know and reevaluate what questions you should ask and what kind of data may answer it.
Work with what you’ve got
If the data you have isn’t doing what you need, it’s tempting to toss it all out and start all over again. But you don’t have to go back to square one on prospecting—work with what you’ve got and enrich your CRM’s database.
Maybe you’re struggling to create customer segments because you have only the most basic information: job role, industry, company revenue. Enriching your database with further information on existing contacts instead of scrapping the list will give you more valuable insights without. Make sure that your existing information is up-to-date and add additional data points that cover firmographics, psychographics, geography, and more.
Red flags: If your emails bounce at a high rate or your information is inconsistent between tools, it’s time to enrich and update that data. Choose lead enrichment software that can deliver contact information and firmographics with reliable accuracy.
Step away from the vanity metrics
Vanity metrics are kind of like cubic zirconia: though they look shiny and valuable, they don’t hold up when put to the test. Metrics like impressions, likes, follower count, and comments might make you feel good, but they don’t correlate to the number of sales you’ll make.
The real diamonds are the metrics that will help you understand which campaigns and tactics actually bring in new customers.
The diamond-quality data that should inform your future decisions or campaign includes metrics like:
- Customer lifetime value
- Average order value
- Conversion rate
Red flags: If your data doesn’t help you solve your problems, you’re probably looking at the wrong information. Not all data is useful for making strategic decisions. Ensure that you look into metrics that are relevant to your problem so your insights can drive you in the right direction.
Recognize patterns and come up with theories
As you analyze your data, look for patterns that are relevant to your problem and start forming hypotheses. For instance, if you notice a pattern of cold calls being ignored, you’ll want to figure out why so you can fix the problem. Start exploring potential reasons for the pattern: maybe SDRs are calling at the wrong time, or they’re using the wrong call intro. Once you’ve recognized a pattern, you can come up with a solution by exploring possible causes of that problem.
Red flags: If your hypothesis is always right, you may be cherry-picking information that supports your original ideas. When collecting and analyzing data, try to remain objective to get the most valuable insights.
Collect, store, & organize data correctly
Collecting data is important, but so is making sure you use it – and organize it – in the right way. You should be able to access the data you need, when you need it. First, the technology you use to collect and analyze data has to be the right fit. Use a data prospecting tool with a reputation for reliable, accurate data so you can trust that your decisions are based on the right information. It’s also important to keep your data organized and stored in a single, easy-to-use place so your teams can collaborate and benefit from data insights.
Red flags: If you have multiple variations of reports and sales data scattered across several systems and applications, it will be difficult to get a clear picture of your important data. Best practices suggest you keep updated information in a single, centralized B2B database.
Segment your audience
Audience segmentation is a tried and true sales tactic where you divide your customer base by industry, behaviors, or common actions or interests. The narrower the segments, the more you can learn. And the more you learn about your audiences, the more effective your marketing and sales campaigns can be for each segment. By analyzing customer segments, you can discover your most profitable customers and strategize how to continue to build better relationships with your buyers.
Red flags: If it’s difficult to draw useful conclusions based on your audience data, your segments might be too broad (or non-existent!). Narrow your focus to draw useful insights you can share with your team.
Don’t forget about context
Context is the background information that influences your interpretation of a situation or data point. For example, if you see a frog in a pond while out for a walk, your understanding of – and feelings about – the situation is very different than if the frog is in your bath just when you were hoping to have a relaxing soak. Context is key in that case, right?
Context is also crucial for getting meaningful insights from your data. Let’s say you sell baked goods and notice a spike in inquiries about pie in mid-March. Without any background information there’s not much you can do with that data. But when you consider the context that March 14 is “Pi Day,” you can better understand the reason for the increase in traffic and adjust your strategies to fit.
Use the 5 Ws to guide you:
- Who: Which prospects or customers are we looking at?
- What: What events or campaigns are our customers or prospects responding to? What actions are they taking?
- When: What time frame did this action take place in? Days? Weeks? Months?
- Where: At what touchpoint (webpage, LinkedIn, or Facebook) did this action take place?
- Why: Was there anything special that took place to trigger this action?
Red flag: If you don’t know what factors are contributing to your outcomes, it’s a sign you need to take a look at the context of your data. Even a slight change to your marketing or sales campaign can change the results, so it’s important to make sure you’re looking at the full picture. Considering the context around the data will lead you to the right answer.
Key Takeaways
- Data insights are the understanding you get from analyzing and interpreting data.
- Marketing and sales teams can be led astray by inaccurate data insights due to difficulty reading or converting their findings.
- You can turn your data into actionable insights by developing steps to proper understanding.
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