Whether you’re the founder of your own one-person agency, or a marketing manager at a 5,000+ person organization, the strategy behind your data insights could improve both your decision-making process and marketing campaigns — which makes prioritizing collect and analysis more critical and challenging.
So what should you focus on to help grow your business?
In this article, we’ll look at why data insights are important, how you can observe and make sense of your data, and some key examples of actionable insights for small businesses.
Table of Contents
What are data insights?
Data insights refer to the process of collecting, analyzing, and acting on data related to your company and its clients. The goal is simple: make better decisions. A good data management and analysis strategy help companies monitor the health of critical systems, streamline processes, and improve profitability.
When people talk about data insights, they usually referring to three core components:
- Data: think about what happens when you go on Facebook. When you log into the app, you allow it to collect information about: where you live, articles you read, places you’ve been, where you went to collect, and other sensitive information. It can even send you push notifications each time you get a comment or reaction. Every time you use Facebook, it sends this unstructured data to a warehouse or database in the form of numbers and text.
- Analytics: So what does Facebook does with all this information? With the help of data insights tools, the social media giant will break your information down into smaller groups to create a detailed buyer persona of you. Facebook can then understand how and why you behave the way you do. They can tell from the data that you’re getting married or planning to buy a house based on your actions in the app.
- Insights: Now that Facebook better understands you, they can send personalized offers to you and other customers similar to you. The platform can now send targeted messages about new homes to buy in your area, or offers for low mortgage rates. Actionable insights are what you learn from data collected, and how your business can use it to improve an objective.
Why are data insights important?
With easy access and visibility to data, it’s easier to process the information and make smarter decisions, faster. Teams can see key metrics into how your company is performing, how successful campaigns are, where your best customers are coming from, and so much more. A smart data strategy can help companies of all sizes improve their bottom line, especially small businesses that need to do more with less.
Four tips for pulling insights from data
1. Accurately display information in a data visualization platform
When showing key metrics from your data analysis, keep your x and y-axis points in proportion. Otherwise, you have a graph that misrepresents the data.
The example below shows interest rates between 2008 and 2012, and while the graph on the right makes the metrics look the same, the graph on the left shows a more accurate picture of what happened.
The right side shows static activity, but the left side truncates the y-axis to represent 3.140% through 3.154%. While the numbers do increase over the four-year time frame, the amount of the increase is insignificant. But at a quick glance, the chart on the left looks like rates skyrocketed.
Data insights can tell a different story based on their visual presentation. Aim to find a technology that collects your data sources and displays them in a clean and accurate way.
2. Identify the right patterns in data sets
As marketers or data scientists, we gather data so we can find patterns in it. Most commonly, numbers trending upwards or similarities between two sets of numbers. Often we spot patterns in a table presentation, or, you can visualize it in a chart such as line graphs, scatter plots, or a time series.
One common pattern to look for when visualizing data is trending quality.
Spotting trends refers to a number that is increasing or decreasing — for example, data on pageviews in your Google Analytics account.
In this case, the numbers are increasing week-by-week, so this is an upward trend. It shows the underlying asset is working. For example, if this is a blog post on your website, you’ll want to create content around similar topics. The uptrend can mean readers are interested.
Now consider this data about organic search traffic from Jan 1st until Jan 25th.
In this case, the number of organic entrances is decreasing week by week, indicating a downtrend. This could mean an algorithm update impacted your rankings, or you need to optimize decaying content.
We analyze data to help make predictions and decisions about a business objective. Look for correlations between uptrends and downtrends and why they exist, so you can better understand and prepare for future situations.
3. Look at the correct time frames
Timing is everything. Errors can occur when business owners look at a quick slice of data and make assumptions without taking into account historical trends. A “slice” could be a month, quarter, or a year, however, you want to look back in time to get a clear picture of what’s happening.
Looking at historical data can provide insight into how your business has reacted to different variables such as economic cycles, seasonality, and market trends. These data points are analyzed for trends or patterns that may align with current conditions so you can make a smarter decision based on fact.
To view historical data, use a data visualization tool that lets you access different time frames easily. When presenting the analysis, you can reference historical trends and tell a more cohesive story for the data.
4. Avoid measuring averages and totals
Consider this: if you calculate the average net worth of everyone in a bar one night and Bill Gates walks in. That number is going to skew upward dramatically. This is where averages can get dangerous. Data points such as these may produce a correlation you want to see, but once Bill leaves the room, you’re going to have a much different story to tell.
Try to stand clear of feel-good metrics like Total Followers or Average Time on Page. These only give you half the picture. To see the full picture, you want to measure percent changes and point lifts depending on what data you’re presenting.
Four key data insight examples for small businesses
1. Customer Acquisition Costs (CAC)
Customer acquisition refers to the process of gaining new customers for your business. Customer acquisition costs are the cost associated with finding and convincing a customer to buy your product or service. This includes marketing costs, advertising spend, employee salaries, overhead, and commissions or bonuses.
What it costs to acquire a customer helps determine the overall profitability of your business. Some businesses are not profitable after a customers first purchase; it may take multiple purchases to go green. You want to keep track of this data to see what campaigns work and which don’t. If CAC is high, it could mean your targeting or ideal customer profile is off.
To calculate customer acquisition costs, take your total sales and marketing spend divided by new customers in a given time frame. Track any changes in CAC monthly or quarterly to see where you can improve your strategy.
2. Buying habits
Buying habits are commonly overlooked as a metric for small business owners. However, understanding how and why your customers buy is important. When you understand your customers, you can create more effective advertising campaigns, tailor marketing communications, and improve the communications strategy for your business.
To better understand customers buying habits, ask yourself the following questions:
- Where do your customers shop?
- How frequently do they buy?
- What channels do they prefer to chat on?
- What are their buying preferences?
- Why do they buy your products or services?
Another data point to observe is: are the products customers frequently buy your most profitable items or are they your loss leaders? If you’re only reselling your loss leader products or services over and over, you’re not going to be in business very long. Understanding this may help you adjust your mindset and start focusing on pushing your customers to your more profitable products and services.
3. Average ticket price
Average ticket price is a great metric to track when pulling insights from data. It covers the average total of every ticket or order placed over a period of time. Even if you have a ton of traffic and a high conversion rate, if your average ticket price is low, it’ll be hard to stay in the green.
To improve average ticket price, you can use upselling techniques to encourage customers or clients to buy more. For example, you can offer complementary products at a checkout page if you’re an eCommerce store. If you’re an agency, you can upsell a Messenger bot with your Facebook ads campaigns. Lifting your average ticket price
4. Marketing Originated Customer Percentage
The Marketing Originated Customer Percentage refers to how much new business is a direct result of your marketing efforts. To calculate it, take all the new customers you received in a given period, and look back at which ones started with a lead that marketing generated. You can use a marketing analytics tool such as HubSpot to track every channel and touchpoint a lead has before they buy.
The average percentage can be between 20 – 40% for the average sales team. If you have a lot of lead generation from marketing, it can be more between 40 – 80%. An alternative way to look at this is calculating based on revenue. It all depends on how you want to look at your business.
Finding the truth in data insights
