New tools to improve performance
on December 29, 2021 (last modified on April 18, 2023) • 15 minute read
Most companies are sitting on a mountain of useful product, sales, and customer data. However, just because you have the data doesn’t mean it is being utilized well.
Sure, you might be tracking some of this data in a spreadsheet and maybe you are creating a report once a week or so.While that’s better than nothing, you are not using this data as well as you could be. You are simply reporting on basic observations.
A better approach is to turn this raw data into actionable insights that your company can use.
In this post, we’re taking a closer look at what data insights actually are as well as some best practices for extracting insights from data.
Before you can understand data insights, you first need to know what data actually is.Raw data is simply a collection of facts, usually in the form of numbers.
Analytics is the actual process of analyzing raw data to look for patterns and trends.
And, data insights is the process of turning your findings into actionable feedback you can use to improve your business.
We can say it looks similar to something like in this image:
So, when we surveyed 40 people to ask about how they used data, it is no surprise that nearly 88% said they use it to extract actionable insights.
And it’s interesting to mention that the process of mining the raw data and then turning them into actionable insights (as opposed to basic observations) does come with a tradeoff. It is harder to pull off and takes more time.
The biggest reason why data insights matter is it allows you to make better business decisions. This can be everything from the macro-level decisions around business revenue, profits, and cash flow to micro-decisions around which ad campaign to run with.
Related: How to Analyze Data: 30+ Experts on Making Sense of Your Performance
It is easy to get overwhelmed when it comes to analyzing data. So, here are tips to make the process of pulling actionable data insights simpler.
Data is only as useful as the question you are trying to answer. Many newbies skip the question step and dive right into the data. That’s a mistake. You should use data to answer your existing question
Aurore Legentil of Synthesio explains, “Uncovering actionable and meaningful insights from data is all about methodology.
First, start with a specific business question (how does my audience feel about my last product launch? What content type has the highest engagement with my audience?), then use human AND machine intelligence to find the answer.
Our best tip: Combine human intelligence and machine intelligence to get insights that are accurate and actionable. Advances in machine learning have made it possible to decode the true meaning of data and human language. But, to ensure relevance, you need human expertise to not only manage the technology, but also bring an understanding of user-generated content, cultural nuance, and local context.
Human-machine teams deliver scale and speed – and ensure insights are accurate and ethical. This is why a hybrid approach to AI-enabled consumer intelligence that combines expert teams and AI-powered technology is best suited for today’s brands that need to make fast, confident decisions.”
Sam Cambell of Reddiquette adds, “Data is everywhere, and it has the power to give you insights about your business. Whether you are looking for information on how many customers have purchased a product or what marketing campaigns are most effective at generating leads, data can tell you. However, this data isn’t always easy to understand. Here are some tips for making sense of your data and getting real insights from it.
Another reason why you should start with a question and have a methodology is that it makes it easy to turn this into a repeatable process. This is not only more efficient for you and your team, but also leads to more accurate data collection and analysis.
For example, Admir Salcinovic of PriceListo says, “At PriceListo, we follow a 5-step process to derive insights from business data. Data cleaning, establishing relationships and trends, statistics calculation, and building advanced analytics models constitute this process.
Data cleaning simply refers to removing duplicate records and finding missing values in newly collected data.
After this is done, we proceed to establish relationships between data points by displaying them in different ways like plotting two variables against another in a graphical representation. Tables also perform huge roles in this aspect.
Once the graphical representation of variables is produced; calculating statistical metrics like mean, median, mode, and standard deviation provide a clearer understanding and a means of comparison of business data provided.
Advanced analytic models are then built using the derived information through tools like decision trees, regression, clustering, and so on. Different models built provide new observations which can help business managers make decisions moving forward.”
Related: Google Analytics Data: 10 Warning Signs Your Data Isn’t Reliable
You also need to make sure your data is clean and accurate. This is often easier said than done though.
Ben Herzberg of Satori says, “To derive insights from data what I think is necessary is the following:
The way you present the data matters. It is easier to tell a compelling story if the data is displayed in graphs, charts, and other visual elements as opposed to a wall of numbers.
“Convert everything into visuals rather than numbers,” says Austin Fain of Perfect Steel Solutions. “How does this help? That’s easy – a person can quickly spot different trends and patterns in visually represented information rather than numbers which can seem a bit overwhelming. Therefore by simplifying numbers into charts, graphs, infographics, it is very easy to gain insights from data. I usually use dashboards, which convert the information into easily readable bar charts and graphs. Not only does this make the work more interesting, but also more useful and fun!”
Like most marketers, you’re probably interested in how much traffic comes to your website and how much of that traffic converts into new leads and customers. But digital marketers want to know more, right? Where is that traffic coming from, and how much of it comes from specific channels, like organic search, social media, and referrers? Which social media and referral sites? Now you can do that deeper dive from one dashboard connected to your Google Analytics 4 account, including key metrics like:
Now you can benefit from the experience of our Google Analytics 4 experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring and analyzing your website traffic and its sources, social media networks and referral sites and pages. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!
You can easily set it up in just a few clicks – no coding required.
To set up the dashboard, follow these 3 simple steps:
Step 1: Get the template
Step 2: Connect your Google Analytics accounts with Databox.
Step 3: Watch your dashboard populate in seconds.
The quality of your data insights lies in both the data you collected (is it credible and accurate?) and the patterns you can extract from it.
“Extracting insights from data is a core process for any business,” says Dan Ni of Messaged.com. “The quality of your insights depends on how well you extract them. It is no secret that it has a learning curve to it but through a lot of trial and error, we found the best approach to do so.
In our case, we employed the use of an appropriate tool such as Google Analytics to help us make this process smoother and the best practice we learned from our experience was that we used to look at the data randomly rather than looking for a specific pattern or trend to better understand if we have upwards growth or the opposite. Hence always look for correlations in your collected data to better assess future situations.”
Donna Duncan of B-SeenOnTop adds, “Look for patterns and anomalies. Ask why. Answers frequently surface problem areas that need to be addressed and missed opportunities to generate more awareness, understanding, and demand for your goods and/or services.”
However, one caveat is to make sure you aren’t confusing data correlation with causation.
“To maximize insights derived from data, you MUST understand what the data is telling you,” says Tyler Specht of SquidCrate LLC. “Correlation is not causation, and if you don’t fully understand the metrics you are using, you can easily assume rather than predict.
To allow the data to be valuable to your organization, understanding what the data means is critical.
If you were to look at the impression of an ad, you should have an idea of how many impressions it typically takes to get a conversion. Then you can take a look at your CPM and derive a strategy to lower your costs.
The same idea populates in our ecosphere. What does the data mean and how can we maximize results? To do so without statistics backing your research, you need to understand the goal of the placement and the scope of the change. Did we make a positive difference in this area of the organization when we implemented this strategy and did we accomplish the goal(s) we set out to hit?”
If you have any historical data, reviewing this can often be extremely helpful in predicting future trends.
“Understand the impact of historical trends,” explains Craig Hewitt of Castos. “A common mistake for business owners is to look at a slice of data and draw conclusions without taking into account historical trends. We analyze historical data to derive insights into how our business has reacted to economic cycles, seasonality, and market trends. Through understanding and referencing historical trends, our data can tell us a more coherent story.”
Related: A Beginner’s Guide to Historical Blog Post Optimization
Another mistake that people make is using a single data point or a vanity metric to inform a larger trend.
“To pull insights from data effectively, use them in conjunction with each other,” says Gregory Yong of Convincely. “Data used in isolation rarely tells the full story, and if you want to make truly accurate observations, you need to pool together multiple metrics. For example, your bounce rate might be artificially high in a given month, and this could be cause for concern. However, data might also point to numerous server outages which left your website offline frequently. This tells you that consumers aren’t losing interest in your product; rather, they’re finding it difficult to access. You can use these observations to confidently rectify the problem rather than mistakenly investing your time and resources elsewhere.”
Just like you shouldn’t make decisions based on a data point in isolation, you also shouldn’t silo off data in your company.
“Using a self-service data analytics platform has been a gamechanger for our team, mainly because it allows users from any department to choose the data and metrics they want to analyze without needing any coding experience,” says James Diel of Textel. “ In the past, getting the IT team to gather the data into a form we could understand cost us a load of time. Now, with the use of reporting software anyone can build an analytics dashboard and report with the click of a few buttons.”
One way to make your data more accessible in your company is to use dashboards or other data visualization platforms.
“My best tip for pulling insights from data is to use a good data visualization platform,” says Daivat Dholakia of Essenvia. “Simply looking at a pile of numbers on paper will be incredibly difficult to sort through and derive insights from, not to mention it will be unnecessarily time-consuming. A data visualization platform allows you to actually see trends and patterns.”
To recap, data is a collection of numbers and facts. Analytics is what you can do with this data. And, insights are the actionable takeaways you are going to implement.One of the best ways to turn more raw data into actionable data insights is to present the data in a way that’s accessible to your entire team (and ideally the entire company).That’s where using software, like Databox, can come in handy. You can pull in data from hundreds of direct integrations and build actionable dashboards quickly. You can also set up scorecards and alerts to get regular updates and act on trends earlier. Get started with a free Databox account here.
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