A key element of small business success is the ability to make solid data-driven decisions that fuel improvement and growth.
But for many, the idea of combing through data just inspires a headache.
If that’s you, don’t worry. In this article, we’re going to show you how using business analytics for data-based decision making can be simple and straightforward.
Why bother? Because using this approach eliminates guessing and gives you a clear path forward to grow your business.
What Do We Mean By ‘Data-Driven Decision Making?’
It may sound intimidating, but what we mean by data-driven decisions is basing your business decisions on data rather than just intuition or common wisdom.
This means using information from your business to make strategic decisions, such as:
- customer data
- trends in sales and marketing
- financial figures
- and so on
The goal of this is to remove the guesswork from your decision-making process so you can …
… move forward with confidence
… always be making progress
… and stop wasting time on methods that may not provide returns
Where Does A Data-Driven Approach Work Best?
There are 4 key areas of your business perfect for data-informed decision making. They are:
Just about any business, no matter how small or unique, has plenty of available data in these areas. The key is knowing how to use it for your benefit.
To give you an idea, here are some of the ways you can use your data to improve your business:
- Increase money – optimise how you make money and how you spend it
- Decrease time – through automation, delegation, streamlined operations, tech stack, team, workflow, etc.
- Increase happiness – for you, your team, and your customers
- Decrease stress and issues – for you, your team, and your customers
Any of that sound like something you’d like in your business? If so, keep reading.
How Data-Driven Decisions Work
Pivoting Your Business
Let’s say you have 500 posts on your blog, but you want to cull them to start over or focus only on a single topic.
Before you “bulk delete” all posts, it’s important to know if any of that content is responsible for attracting traffic, leads, and sales to your business. As well as how much impact the removal of that content will have on the business.
Unless you check the data, you could be deleting blog posts responsible for bringing in a lot of traffic, leads, or even sales into your business. And all of those leads and sales would suddenly stop if you remove the posts before you’re ready. Yikes!
This is where data-driven decision-making really shines. After reviewing the data, you can decide whether to:
- leave those select posts in place that work for your business
- remove them at a later date
- rework them so all is not lost
- or redirect those posts to your new offer.
But if you just delete the blogs, that traffic goes to a broken link and you lose customers.
Fixing A Low-Performing Sales Page
Here’s another common example. Perhaps, after a few weeks of promotion, you decide a sales page isn’t converting well enough. So you want to change the copy, the imagery, the page design, the offer, or the price.
This is a perfect place to look at the data before making any changes. Because if you want to make an effective change rather than just flinging spaghetti at the wall to see what sticks, you need to know whether:
- People are looking but not buying or just not looking at all
- Viewers are stopping halfway down the page before they reach the critical information
- The wrong people are looking at the page
- Your buy button doesn’t work
- or Yes, maybe the offer or price is not the right fit
There are so many variables! It’s critical to look at the data first before making a decision about what to change.
5 Real-World Examples Of Data-Based Decision Making
Example 1: Low-Performing Sales Page
Something similar happened to a client of mine. She had created a quiz that looked great in testing so she started pumping ad traffic towards the quiz page. But the page conversion rate was really low.
She thought it must be the quiz title, the image on the landing page, the size of the font, or maybe her ads or ad targets. So she tried out a variety of changes based on these guesses but she continued to get the same weird result.
So we looked at the ads data and the data of the page. The ads were doing their job but a large percentage of people left the site in less than a second. This is not good and highly indicative of a technical error on the site. With a little investigation, it was discovered that the embedded quiz wasn’t displaying on Apple devices or in Safari.
Result: Once we fixed this, the conversion rate shot back up to normal. The problem had nothing to do with all the changes she had made which just cost her time and money to implement and test.
Example 2: Why Is Nobody Booking This Retreat?
Another client had a retreat promotion running for a while but buyer numbers were waaaay down and they couldn’t figure out why.
Looking at the website data, there had been over 3k viewers of the page (a solid number to deduce results from) of which the majority had spent a long time on the page (assumed reading the content and watching the video).
Now, we know that people who aren’t interested leave pretty quickly. But that wasn’t the case here, plus the returning visitor numbers were a lot higher than normal. This shows genuine interest … there were just no buyers. It looked like people were interested in the offer, but maybe not the timing or location was off. Or, they were potentially waiting until the last minute because of COVID lockdowns.
Result: After ruling out technical issues, we added a “register your interest in future events” sign up form to the sales page. Suddenly a lot of that engaged traffic began to flow into the business rather than stopping dead at the sales page because the date wasn’t right. This resulted in enquiries for other events and private 1:1 bookings, it also built their list for future marketing. Without reviewing those page stats and visitor behaviours the client would have cancelled the event and lost a lot of sales.
Example 3: Dropoff In Application Submissions
This client noticed a dramatic drop-off in application submissions and couldn’t work out why. Applications were the lifeblood of her business and she used to get 10-15 per month but now was only getting 2 per month.
She’d tried investing in advertising on different channels, different marketing strategies, surveying customers, nothing was working.
When we reviewed her website data and application process we could see where people were dropping out of the application funnel and off the sales page.
We tested some language changes to the copy, made the initial application form a lot shorter, added 2 more application buttons in prominent places on the page, and updated the application follow-up email sequence to improve engagement.
Result: This client went from 2 applications per month up to 35 applications in the very first month after the changes.
Example 4: Operations Optimisation
From an Operations point of view, another client had a VA spending hours of her time each month collecting data and sending independent reports from each application and touchpoint, including:
- social media channel reports
- sales reports
- profit/loss report
- affiliate report
- email marketing report
- CRM report
- complete and overdue tasks report
- customer service report
- Facebook ads report
- Pinterest report
- Google ads report
- website analytics report
It was costing the client a lot to have these reports generated that still couldn’t give her the full picture of how her business was going.
When we came on board to determine the data reporting process, we also discovered the client’s tech stack was causing tons of frustration every month to:
- use all these platforms
- link them all together
- get clear data reports from each
- manage the customer service and tech support for ALL of the platforms
It was a ticking time bomb for her business and actually costing her a LOT of money without realising it. She’d just tapped on extra apps as the need arose as her business grew without ever reviewing what else could be possible.
Result: Streamlining the business into fewer applications and devising a central data source for faster and clearer reporting saved her over $9500 in annual system fees. Not to mention the report gathering and interpreting time.
Example 5: Advertising Optimisation
This is an area where a lot of people go wrong, especially when they make decisions based on their feelings instead of data.
One of our clients ran the same Facebook ad campaign since 2018 and called it her ‘unicorn’ ad that just kept delivering. But after closer review, the lead cost had increased from only $0.40 per lead to between $2.50 – $4.50 per lead.
And the worst part was that most of the leads went into her email funnel but did not open emails and rarely bought her products. The ROI was actually in the negative.
Because she focused on the list growth and had surpassed 10,000 subscribers, she completely missed the fact that she was not getting the financial return she expected – which was the actual point of growing her list.
Further to this, her list of now 10K+ was the number she used to forecast her next launch based on a low industry benchmark of 1% conversion. This is the typical way to do it. 1% conversion of 10k = 100 sales. Let’s say 100 sales @ $100ea = expected income 10K. So you plan a % of this income to go to ads, team, copywriter, etc.
But when we reviewed how many of those 10K subscribers were actually cold leads, how many had bought something similar previously, and the likelihood of repeat buyers …
The results were staggering – she actually had only 1,800 recently active leads.
Which changes the conversion number completely – 1% conversion of 1.8K = 18 sales. 18 @ $100ea = income of $1800.
To put this into perspective, if you budget based on the first scenario income of $10,000, you might invest 20% in creating and launching the product leaving your actual result in the negative. We also discovered that almost 50% of the active database were peers rather than ideal clients for the launch she had planned, which dropped the income goal even lower.
Result: Despite the surface-level gains she thought she had achieved, the reality was a big financial loss which could have been discovered much earlier if she had been checking the data.
It’s Time To Start Using Data-Driven Decisions In Your Business
As you can see, digital marketing is a really good example of using data to make decisions because the preliminary data is usually right at your fingertips (sales/clicks/leads). But there are many other areas of your business where this same process applies.
These stories show that taking a deeper dive into your data is necessary to truly understand what the numbers tell you about your business. Following the data is a better way to drive improvement and growth in your business than trusting your gut.
So if you’re ready to make the transition, here is how you get started.
Step 1. Consider The Stage Of Your Business
The data you will focus on depends on the phase your business is currently in. Which of these fits you best?
In this phase, you are usually focused on marketing and sales. Operations and Customers are still being worked out. You want to know if what you’re offering is wanted in the market and how best to sell it.
Once you’ve reached the Growth Phase, you are usually focused on all 4 areas, although commonly I still see Operations and Customers as the lowest priority. However, this can be a big mistake because focusing on those areas at this stage can produce some amazing results.
When it’s time to scale your business, that’s when Operations and Customers become the primary focus. At this stage, business owners want to clean up the mess created during startup and growth so they can streamline and get things running systematically.
So, your first step is to decide which phase you’re in and use that to decide where your focus should currently be.
Step 2. Collect Data
After you decide where to focus, the next step is to collect your data. Start with the most important area you will focus on for now, then move down the list.
The data you collect and how you collect it will depend on your business. But one thing is for sure – check your data and don’t make decisions from limited data.
Note – there is such a thing as TOO MUCH data, as well. This can hinder your decision-making process and actually cost you more time, resources, and/or money than necessary. So try to find the sweet spot of enough but not too much.
- brand awareness
- credibility, authority
- network growth/reach
Where To Find Marketing Data?
- social media followers
- social media engagement
- list growth and unsubscribers (for each list/lead magnet)
- Google Analytics for your website:
- traffic and % of return traffic
- page views per session and time on site
- sessions per acquisition channel
- goals/conversions per acquisition channel
- money brought in
- how money was brought in
- time to make a sale
- the flow/process to make a sale
- common hurdles faced
- conversion rate
- avg customer lifetime value
- avg customer repeat purchase
- cost to acquire a lead
- how long until a customer buys again
- growth of customer #s over a period of time
- % of repeat buyers
Where To Find Sales Data?
- revenue – per product stream
- expenses – regular / Adhoc
- ad spend
- ad ROAS
- communication flow
- app/system usage
Where To Find Operations Data?
- team check-in
- workflow review
- cost/output review
- automation and UX testing/review
- satisfaction levels
- customer service usage
- user/customer experiences
- customer success
- retention rate
- customer habits
Where To Find Customer Data?
- customer journey audit
- reviews or feedback forms
Step 3. Review Your Data
For each section above, review the data you have available and consider what trends, opportunities, or problems you see that can be actioned.
- Note the problem areas
- Look at the goals
- Outline the touch-points
- Identify potential questions/issues that may be faced
- Identify the data to collect to work out what’s happening at each touchpoint
- Collect and collate recent and past data
- Make notes of events and changes (world, local, business/marketing changes, technical issues)
- Review for problem areas, growth areas, and hidden opportunities
Remember – the questions you are trying to answer are:
- How can your business work smarter?
- How can you optimise for growth?
As you review your data, look for indicators that help you answer those questions and develop some action items.
Get Started Using Data-Driven Decisions In Your Business Today
I invite you to set aside a little time this week to try this analytical approach. Do an audit of these 4 pillars against your business objectives for each.
Marketing. Sales. Operations. Customers.
Collect the data and then review it to discover actionable insights that lead to confident decisions. To make it easy, grab these free resources:
- CX Audit Tool
- Google Analytics Performance Indicators
Or, if all of this still gives you a headache, book in for a consultation here.
We’d love to help you make sense of your business data so you can start making data-driven business decisions and reap the growth benefits.