Data-driven in 2021

In the last few years, more and more companies are branding themself as being data-driven. But what does it mean to be data-driven, and how do you become data-driven in 2021? That is what this article is all about.  

 Being data-driven refers to the incorporation of data or information that is stored and rooted in numbers and fact into an individual or organization’s decision-making process.

 

In other terms, data-driven companies are using collected data from example: sales, website visitors, production, etc., in the decision-making.  

In the last couple of years, the way to work with data has been divided into two categories: Data-driven and data-informed. 

Data-driven

Your data is your best friend when making decisions about creating a new business strategy or how you should sell a specific product. When you work data-driven, you don’t get affected by personal feelings or insight, and your focus is less on the bigger picture. 

Let us make an example from the online marketing industry:

You want to sell more of one of your cash cow products, and therefore you are planning to run two different marketing campaigns; Campaign A and Campaign B. The purpose of each campaign is the same: sell more of one specific product. 

You only need and have the budget for one active campaign, but you chose to run both campaigns in the beginning to get the data on which campaign is creating the best results. 

After a week the Click-through-result (CTR) for each campaign are: 

Campaign A = 2,5% CTR

Campaign B = 3,3% CTR 

The data talks for itself, which is why you are pausing campaign A and going with campaign B. 

Data-informed

If your company is data-informed, you use your data together with your unique experience and know-how from your customer’s behavior, sales, etc. that means that you use the data as one part of the decision-making process. 

We are again looking at campaigns A and B from the previous example. 

If you are 100% data-driven, you will stop campaign A because of the lower CTR. But when using the data-informed approach, you are looking at different factors to decides if the campaign should be paused. You are looking at factors like:

  • The campaigns cost-per-click (CPC): Maybe campaign B CPC is twice as expensive as campaign A.

  • Results from previous campaigns: Maybe a CTR at 2,5 % is higher than the results your campaigns typically give.

  • Your customer’s sentiment for each campaign: Are people clicking on campaign B because they like the content or because the content is encouraging them. 

The usual saying is that you should be both data-driven and data-informed – if you want to make the right decisions in your business. I agree that companies need to use both of the data-ways. But as a marketer, who has been in the industry for six years, I can see that most companies only are using 20%-ish of their data. They have programs like Google Analytics to capture their customer’s online behavior – and take decisions based on that. 

 But by looking and analyzing, for example, sales data, you can get a more profound and better understanding of customer behavior. 

To reach the point where you can understand and use 100% of your data – you need to start working with data science. 

 

What is data science?

Data science is becoming the new hyped word and the new big thing, even though that data science saw the daylight for the first time in 1962 when John Tukey introduced the field of “Data analysis.” 

Data Science belongs under the AI umbrella. In short, data science is a way to look at historically collected data like e Point-of-sales, employee data, stock data, product purchases, CRM data, etc., with the purpose of identifying patterns, there can be used to predict how the future will be like. 

This could, for example, be forecasting when a customer buys a specific product, forecast when an employee will call in for sick, or identify which sale-product there will lose market share in the following years and why this will happen. 

So, if your company wants to get the value from your data, you should add an extra ‘data-layer to the statement: We are working data-driven. 

How data science works

Let us use the same marketing campaign as mentioned before, but now with the extra data science layer. 
Before we are thinking about creating the campaign, we analyze all the customers who already have brought the specific products we want to sell.

By collecting all the sales data for each purchase, we can get an actionable insight into what customers are buying this product. To create the best result, the sales data must contain:

  •  The total price of purchase
  • The product brought and amount
  • Customer identification number
  • Address and city
  • Time of purchase. 

Using a data science algorithm, you can know to identify patterns for the customers buying that specific product. 

It could be patterns like:

  • 82% of all women buying the product is between 35. And 38 years old.
    • 60% of all do purchases of the product is made on a Monday between 14.00 to 19.00
      • 92% of all of these sales are made from a tablet.

         

  • 75% off all men buying the product is between 25 and 28 years old
    • And 45% off all this segment buys product BB in the same purchase

       

  • 82% of all the buyers of this specific product live at a distance of 50 miles from your physical store.
    • And 23% of all these product buyers are buying the same product 3 weeks later from their computer.


The above are just examples of the patterns you may identify. It just gives you an example of how data science can give you a deeper customer understanding than the one you can get from Google analytic. This kind of data science analysis is called a forecasting analysis. 

Borbaki’s data scientist have made a forecasting analysis for a product machine production company, where they increased the revenue pr. Existing customer with 65%, read about the case here. 

Using the insight from the data science analysis, you can now create different campaigns that target the people who most likely will buy the product. This will affect your CTR, CPC, POAS, ROAS, and sow on positively. With the data science way, are you one step further down in the sales funnel from the start.  

But this marketing example of how data science can improve workflows and create insight is only a small-selected area. The forecast analysis has a significant impact on all departments, and if implemented correctly, it can increase early revenue and save the company a lot of money. 

Data science affects all departments

When you know how your customers buying behavior is not only optimizing your marketing campaigns. The insight gives your company the ability to streamline and improve the work in all company departments. The forecast analysis gives you the ability to predict and optimize: 

  • Finance: Set the proper budget and cash flow forecasting. If you want to know more about how data science can affect your budgeting, I will recommend reading this article about budgeting.

  • Sales: Reaching the prospect there most likely will buy the product. You can read more about how to implement data science analysis in sales here.

  • Stock: Optimize the stock management because the forecast tells you how the product demand will be and, therefore, how many products you need to have in stock.

  • Strategic planning: The ability to contact customers there isn’t following the typical buying behavior. You can therefore get an insight and understanding of why some customers churn and change it. Are you a business executer and need an understanding of AI, Machine Learning, and data science. I will recommend you read this article about how you can benefit from data science as a business executive. 

 

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