The word ‘data’ can still be daunting for the sort of people who do not consider themselves to be ‘numbers’ person. Meanwhile, there are certain groups of people who do not base their decision-making processes on statistics and data and who instead make decisions on far more risky assumptions. In this article we will discuss how data driven decisions can help your business grow.
Sadly, the assumptions are how the majority of businesses work. As shown in a study by the Social Listening Platform Mention, fewer than 15% of companies have a data-driven community. Only 17 per cent of those questioned said they have a high level of data literacy, which means that they feel safe reading, developing and communicating data as information.
How to Benefit from Data Driven Decisions
Good news? Such disparities are chances for you to gain a foothold by building up data on more of your company activities. This is how to get started on getting benefit from data-driven decisions:
Record the different types of data you are receiving
A lot of data is created by everyday activities and encounters with clients. You can’t even make use of it if you don’t know what is already open to you.
Many sources are obvious: if your store uses the Square POS platform, you’re gathering titles, types of bank cards, time to buy, and much more. Other sources of data are less apparent: if you put Ads on Facebook, you can look more deeply than conversion rates. Who’s clicking on the advertisements, when, and on what devices?
A quick glance at the Square datasets could reveal that most of your clients are loyal customers. It could encourage you to start a loyalty program to award your customers. With Facebook data, a sort of post can float to the top and encourage you to try to put a few ads with the same sort of post.
That’s just skimming the surface of the data you are likely to be gathering. Think about the opportunities if you’re willing to gather other data.
When you know the volume of data you’re already acquiring, it’s simple to get bogged down or overwhelmed by all the accessible metrics. Remember your company goals, then think about through data you really need to track.
Think you’re running a cafe. Even though the cost of the ingredients is important, they are not relevant to the issue of whether you can open a drive-thru-only place. What is the average time your staff spend serving the client in the window opposed to the counter? What service channel has a higher average order quantity?
When you have a target in mind and you are gathering data, the next move is simple: make time for a review.
Block Off Time to Review
Without analysis and review, the data are statistics which do not translate to changes. ETL — short for “Extract, transform, load “— enables you to place those figures in a software that tells a plot.
Make time on your calendar every week to review the most recent improvements to the metrics you are tracking.
Various data sets demand different tools for interpretation and visualization. A word cloud may be suitable for testing the trends in customer feedback on your blog. Regression analysis is more fitting if you are trying to create a connection among two numerical factors.
Take a look at the bigger picture
Analyzes can be performed at different stages. When you want to see a big picture, analyzing results from different data sets is important.
Say you would like to know what types of clients are most efficient. Ok, you can’t just worry of which people are paying you the most. How much will it cost to serve these types of clients? What is their average lifetime value?
Amazon keeps track of 500 KPIs so that it always has the details it wants to make a choice
Responding to your core issue requires a quantitative analysis, which can be difficult. It is especially important to evaluate the variables dependence on others: in the earlier example, does the client’s average lifetime value correlate negatively with per-session spending? Seek help when in doubt.
Offer the keys to your team
Once you’ve compiled and analyzed the data, there’s no need to hide that from your team. You just can’t make every decision for your business, as you might like to.
Invest in learning, please. Your team needs to learn how to locate, view and produce reports to your database.
Care about communication, too. Build a set of common terms. Bring everybody up to speed on why you’re putting new focus on data analysis.
Lastly, priority should be given to collaboration. Motivate team members to bring unexpected results to your attention. Reward them for introducing data-driven ideas — such as a new product or service or an undiscovered target market — to your attention.
Begin Demanding Data for Decision
The greatest obstacle to become more data-driven is cultural: when you want to address a business problem, everybody has to listen to the data and make data-driven choices.
Data addiction is a key to Amazon’s growth. The eCommerce giant keeps track of 500 KPIs so that it always has the details it wants to make a choice. Some of Amazon’s initiatives begin by recognizing patterns within them, such as the link among slower page load times and reduced visitor engagement.
Lay out a plan for how exactly, you’re going to retrieve data. Set the parameters for the volume of data needed and the timeline samples to be taken. If you are a cafe that wants to optimize its menu, for instance, you can’t say that what’s ordered for dinner on Thursdays is indicative of the whole week.
It’s hard to become a data-driven business. Yet ask the members of larger companies, and they will still tell you: it’s a lot simpler to do when you’re small than when you’ve expanded.