If you don't consider yourself a "number" person, the word "data" can be intimidating. But if your decision-making process is not based on numbers, make them on something much more dangerous: assumptions. <! – ->
Unfortunately, most companies make assumptions. According to a survey by the Social Listening Tool Mention, less than 15% of companies have a data-driven culture. Only 17% of respondents said they had a high level of data literacy, which means that they feel comfortable reading, creating, and communicating data as information.
Supporting data-driven decisions
The good news? These gaps give you the opportunity to improve by integrating data into more of your company's business operations. To start supporting data-driven decisions:
1. Inventory the types of data you collect
<! – -> Daily activities and interactions with customers generate a lot of data. If you don't know what is already available, you cannot use it.
Some sources are obvious: If your business uses a square POS system, collect names, types of credit cards, when to buy, and more. Other data sources are less obvious: when you run Facebook ads, you can look deeper than conversion rates. Who clicks on the ads from where and on which devices?
A quick look at the Square record may show that the majority of your customers are repeat customers. This could inspire you to start a loyalty program to reward your regulars. With Facebook data, some type of post can go up, causing you to run some ads with the same type of post.
This only scratches the surface of the data you are probably already collecting. Think about the options if you intentionally collect other data.
2. Stay focused
When you consider the amount of data you already collect, you can easily get distracted by all the available metrics. Consider your business goals and then figure out which numbers you really need to monitor. <! – ->
Suppose you run a café. Although the cost of ingredients is important, they are not relevant to the question of whether you should open a transit location. How much time do your team members spend on average operating a customer at the window compared to the counter? Which service channel has a higher average order amount?
Once you have a goal in mind and gather data, the next step is simple: take the time to review it.
3. Locking period for review
<! – -> Without verification and analysis, data are only numbers that do not lead to changes. ETL – short for "extract, transform, load" allows you to insert these numbers into a program that shows a story.
Take time to review your schedule once a week to review the latest changes to the metrics you monitor.
Different data sets require different analysis and visualization tools. A word cloud can be used to check trends in customer comments on your website. A regression analysis is more useful when trying to find a correlation between two numeric variables.
4. Look at the big picture
Analyzes can be carried out on several levels. Reviewing results from multiple records is important if you want to see the big picture.
Suppose you want to know which types of customers are the most profitable. You can't just think about which ones will pay you the most money. How much do these customer types cost for service? What is your average lifetime value?
Answering your core question requires multivariate analysis which can be difficult. What is particularly important when analyzing is which variables depend on others: in the previous example, does the value of customer life correlate negatively with the expenditure per session? If in doubt, ask for help.
5. Give the keys to your team
Once you've collected and analyzed data, there's no reason to keep it off your team. As much as you want, you just can't make every decision for your business.
Invest in training. Your employees need to know how to access your database, interpret the data, and generate reports.
Also think about communication. Create a common set of terms. Make everyone up to date on why you are giving data analysis a new focus.
Finally, prioritize collaboration. Encourage team members to alert you to unexpected results. Reward them for drawing attention to data-inspired ideas like a new product or an undeveloped target market.
6. Request data for decisions
The biggest challenge to becoming data-driven is cultural: when you have to answer a business question, everyone has to access the data and make data-driven decisions.
Data obsession is a secret to Amazon's success. The e-commerce giant monitors 500 KPIs so that they always have the information they need to make a decision. Many of Amazon's initiatives begin to identify trends between them, such as the correlation between slower page load times and reduced visitor activity.
Develop a plan for how exactly you retrieve the data. Set parameters for the amount of data required and the period in which samples are to be taken. For example, if you are a restaurant looking to simplify its menu, you cannot assume that what is ordered for dinner on Thursday is representative of the entire week.
It is difficult to become a data-driven company. But ask leaders in larger organizations and they'll tell you: It's much easier when you're small than when you've scaled up.
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