5 Ways to Understand Customer Behaviour Through Data-Driven Marketing

5 Ways to Understand Customer Behaviour Through Data-Driven Marketing

Emma Miller
16 May 2017
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Once upon a time, marketing experts relied on past experience and their own intuition in order to understand the consumer. By using ex-post methods, such as focus groups and polls, marketers built an image of their customer and had to crunch limited available data to try to envisage how their product will position itself against the competition. This has changed immensely with services such as Facebook, Amazon and Netflix, which showed first-hand how data-driven marketing cannot just help understand the consumer helping to drive consumer behaviour and substantially increase gains.

But, how does this concern companies that do not have massive amounts of cash on hand for such activities?

The available data is massive

There has never been a time before now where more data was available for analysis. If we just take a look at how much time we spend online, with Brazilians taking the lead with 5.2 hours online through laptops/desktops and 3.9 hours on mobile devices, it’s easier than ever for marketers to follow digital bread crumbs to gather and analyse data on customer behaviour and come up with new strategies for their chosen niche.

The much-lauded leader in this field in the past couple of years has been Lenovo. According to them, they managed to develop a predictive model which helps them assert if a visitor to their website is going to buy one of their devices in a matter of seconds. By using this data, they carefully position customised content for the visitor with an accuracy of almost 90%.

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It’s based on scientific analysis

Even though the times have changed, the founding principle of data-driven marketing is still a very old one – understand your customer and their needs. There’s still no definitive answer on how to predict human behaviour. The rule of the thumb is finding data which can be analysed and segmented, and thereafter expanded upon. For that reason, there are essentially three steps that need to be taken for a proper analysis to be conducted.

Customer research:

The first condition is to understand your consumer demographics, their interests, search analytics and how all these can be merged into forecasting buyer decisions.

Segmentation:

After initial research, it’s important to divide your customer base into segments and prioritise target audiences who have the most interest in your field, particularly customers with intent to buy.

Third party data:

In order to have a complete overview of your market, competition analysis is essential. Add to that the analysis of consumer behaviour online and a company can be said to be prepared for connecting with and understanding their target audience properly.

Data-driven marketing is thus grounded in creating the image of the perfect consumer based on real people who are analysed, thus focusing on influencing their decision-making process and attempting to predict their behaviour over time.

How it’s done

After the in-depth analysis of consumer behaviour has been conducted, the question that is naturally posed is – how can you actually predict anything?

We all buy certain items at certain times in our lives, the so-called life stage based purchases. Every time we buy a new car, a home, marketers can use this information to predict complementary purchases. By using this data effectively, they can target consumers by handing helpful articles or recommendations to increase sales.

This still might seem like some kind of dark art, but it is actually entirely based on what we do. Even though it is not possible to look for “people who just bought a new car”, it is possible to analyse existing databases, see people who have, at some point, bought a product and backtrack the behaviour that led to the purchase. By correlating this data with age, gender and income, it’s much easier to segment the market and find connections, consequently making much more successful predictions for the future.

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Interest and intent are not the same

This is the trickiest part of data-driven marketing. It’s much easier to understand online behaviour and interests of target demographics than the intent to buy. It is far different to just know that someone will buy something than knowing when that person is going shopping.

This is where connecting databases is essential for marketers. If a company is able to create a data partnership and gather information on shoppers from other websites and services, it will have an easier time measuring consumers’ intent. The universal wisdom here is as follows – if a person is looking at articles about cars, we can safely assume that the person is interested in cars. But, if that same person clicks on product pages of actual used and new car lots, this is a much better indicator of intent to buy.

It can do wonders for any business, anywhere

If you have a system set up, the information derived from it about your customers and their habits is a virtual El Dorado, limited only by your imagination and the skills of your data scientist. For example, a company with a telephone answering service which can correlate their customer insights at the door, so to speak, can achieve an incredible boost to their business.

The implications are endless, whether they are connected with empowering your sales department by giving them hot points which will result in a buy or your R&D by giving them insights into what your customers expect from your product.

Data harvesting is the new way of doing business, period.

About the author:

Emma Miller is a marketer and a writer from Sydney. Her focus is digital marketing, social media, start-ups and latest trends. She’s a contributor at Bizzmark blog and a mother of two.