Using Data Analytics Effectively in Marketing

   Using Data Analytics Effectively In Marketing - Ambitious PR                                        

Data analytics in marketing is essential for any modern business. If you don’t have data analysed in the right way, you can’t understand your customers, and if you can’t understand them you can’t reach them. So, if data is good, more data must be better, right? Not necessarily. As  puts it: “We have huge lakes of data that are filled with dead fish”. Whether you’re designing a mailing list or training a neural net, you should never start with data first.

Many businesses gather as much data as they can, pile it all in a database, then try to think of ways to use it. This is as backward as buying an encyclopedia then wondering if there’s anything you can look up in it. As marketers, our first job is to work out what questions we need to ask. This can be much harder than it sounds!

What can data analytics in marketing tell you?

Data can only ever answer with numbers. It can tell you exactly how many people used your website in the past month and where they came from, but it can’t offer any qualitative insight. Marketers, therefore, need to break questions down to a point where they can be answered with numbers alone.

For example, you might be looking to increase traffic to your website. No matter how much data we have, it can’t tell us how to do this. Our job is to design questions that, when answered, might point out how we can achieve this, such as:

• How many people visited our website last month?
• Which sites did they arrive from?
• What demographics do we see the most of?
• Which web pages are the most visited?

Keep in mind that we haven’t looked at any data just yet. We aren’t starting out with a big pile of data and trying to work out what it can tell us, we’re coming up with questions first and then looking for the answers.

Select the data you require

Now, with our list of questions in hand, it’s time to find some answers – let’s get some data! Think about what information you need to know in order to answer the question, then how you can get it. For instance, if we want to know where our site users have arrived from, we’ll need to set up some form of a tracking system.

If instead, we wanted to know about the types of products that are often bought together, we could gather the information from our sales database instead.

Quality is important, and you’re often better off getting detailed, relevant information on a targeted audience than casting your net as wide as possible. As we’ve explored previously, customers are often willing to give you their data if they trust you, so you should use this to build up a nuanced picture of your audience. Consider too, what data does google analytics prohibit collecting? What data analytics KPIs do you need?

Add human perspective

Going back to our first example, we now know how many people use our site, which pages are most popular, where referrals come from, and so on. Now we can interpret them: Tobaccowala highlights the importance of human insight using what he calls ‘The Four P’s’: Perspective, Provocation, Point of View, and Plan of Action. It’s our job, as humans, to turn the dry data into useful perspectives.

Digital marketing data analytics can tell us that our website is getting a lot of referral traffic from Facebook – our insight is that this may be an area to focus marketing spending on. This insight generates further questions – how much should we spend on Facebook advertising? Which demographics are the best ones to target?

Can we produce better content for the platform? Go back to the start and repeat the same steps we’ve just been through until you have specific actions you can take to achieve your overall goal. This process is a cycle, and it’s crucial to making informed decisions about your marketing strategy.

A case in point

Take the example of Sinan Aral’s work with Jet, an online sales platform. His goal was to improve overall sales. Rather than dive into the data, he came up with specific questions to ask – “which products are often purchased together?”, and “which products are rarely purchased together?”. Simple questions with quantitative answers, which he could answer by turning to sales data. A simple tweak to the ‘suggested products’ algorithm improved the likelihood of displaying shoppers products they’d be interested in purchasing, ultimately improving sales.

This approach is efficient, cost-effective, and it makes use of marketers’ most important asset; their minds. The cycle of data-driven research delivers consistent results, and allows marketers to demonstrate real value to clients and management:

• Set a high-level goal.
• Identify specific questions with quantitative answers – “how much”, “how many”, “how long”, etc.
• Acquire data that can answer these questions.
• What do these answers tell you? If there are further questions, begin the cycle again.

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In the digital age, brands must be able to use data effectively and efficiently. If you need help with data-driven insights and creative thinking, we’re always ready to help.