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How to Use Behavioral Data in Optimizing Customer Experience

Behavioral data is all the rage nowadays. How long do you spend looking at a particular product? Do you add it to your basket but then leave it there for a week or two to see if your wallet can be persuaded to part with some of your hard-earned money?

When offered free delivery if you add more to your cart, do you go for it, or do you turn it down? Which particular advert was it that enticed you to further investigate a particular product? Are you ever hesitant about making a purchase due to online payment fraud?

These are the things that behavioral data covers, and more. If you've ever wondered how targeted ads are becoming so accurate, to the point where you're convinced your devices are spying on you or have developed telepathic capabilities, then you're well aware of the effects of behavior data collection, analysis, and implementation.

It might sound complicated, but don't throw in the towel just yet and start googling things like "how much do affiliate marketers make" in a bid to change careers. We've got you covered.

Most shoppers want a personalized experience

With 80% of shoppers wanting a personalized customer experience, it's no surprise that the likes of Netflix, Amazon, and Spotify, among many others, invest time and energy into providing exactly that. While it might feel a bit on the creepy side to have your desires so well anticipated, and your data so carefully monitored, the result is a better customer journey.

 

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Behavioral data isn't just about the "what"

Behavioral data isn't just about what you, or what the people with the similar IP address as you look at. It's about looking at the finer details of your interactions with products and websites, assembling an entire consumer profile of your behaviors, and comparing it to other users with similar interests and behaviors. This allows companies and algorithms to understand you deeply and give you a relatively personalized consumer experience.

The more personalized and relevant, for example, music, movie, or product suggestions are, the more likely you are to purchase or consume them. The main aim is to keep customers by keeping them happy.

How can we learn from the companies that use behavioral data?

Let's dive into some steps you can take to optimize the user's journey with behavioral data.

Decide on your goals

In order to successfully use behavioral data to your advantage, you first have to define what success looks like. What do you hope to gain from gathering and analyzing your customers' data? Do you want to increase sales by a certain percentage? Do you want to get your customers to engage with your service for more hours in the week? Do you want your customers to leave you more good reviews or refer friends to your services?

Whatever you decide, make sure that your goals are SMART - Specific, Measurable, Achievable, Realistic, and Timely (or some variation of these definitions).

Another thing to bear in mind is that you ought to focus not only on the end results you want to achieve, but uncover the best methods for reaching those - what works and what doesn't. So perhaps a goal could be to find out which key phrases get the most attention from customers, or which retention methods are most efficient.

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Narrow down your KPIs

Once you've decided what your goals are, look at how you'd like to track the success of those goals by choosing relevant KPIs (Key Performance Indicators), such as your ecommerce conversion rate. This will help you to quantify your success rates with clear, and measurable indicators.

Pick your data sources

You can obtain large data sets from your own organization's resources. Websites and social media don't just have low-cost marketing strategies; they also have a ton of data you can look at and pull together to create a comprehensive view of your customers. However, you also have the option of hiring an outside company to create customer profiles for you.

Customer service teams can also offer valuable insights into your customers' needs and preferences. Real-time consumer data, such as how long people stay on a page or how many times they go to check out before either canceling the order or clicking "Buy now", is invaluable.

Artificial intelligence such as machine learning and natural language processing are also invaluable tools for understanding the more complex trends in human behavior and take out much of the legwork required to assemble a clear and reliable picture of your customers.

AI can also help you in differentiating between different users in the same household or on the same device on the basis of behavior patterns, for example.

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Figure out your customers' paths of desire

A path of desire usually refers to a path off the designated lane, created because it's more convenient than the one primarily laid out. It's the shortcut through the gap in the hedge next to the mall. If you want to optimize your customers' experience, you need to see where their paths of desire are.

At what point do they meet a roadblock and decide they can't be bothered with this path anymore? That's where you need to make it easier for them to reach their destination - purchasing your service or product, sticking around via social media, becoming an advocate for your brand. You need to remove or alter any roadblocks.

When you figure out what the most common path of desire is for your customer - how they get lured to your page, what they do when they get there, at what point they're likely to abandon the mission - you can develop a more convenient path for them to travel along. Perhaps you'll find that you need to switch your affiliate marketing program to a different supplier.

Sometimes the path of desire isn't the most direct route either - sometimes it's the scenic route - some customers like a good product and brand story they can engage with and aren't just looking for a purchase.

Find out what sets your customers apart

You can assign a unique identifier to each customer so that your AI can figure out who is on which path, and not assume everyone with the same IP address is the same person. Otherwise, your 55-year-old cis male dad might start getting advertisements for period products.

Using a unique identifier also means that when customers switch devices, you can still figure out who they are on the basis of things like email addresses. It's not a perfect system but it goes a long way to helping personalize the user experience.

 

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Decide what to monitor

Less is more with data sometimes. You should pick a few key areas to monitor and gather data on and share this with your team. You'll need to make a proper plan with your team in order to have an organized and useful data set rather than a mishmash of irrelevant information.

Making a plan will allow you to analyze the results with more clarity and the knowledge of how the information can be useful for the customer experience. You can use behavioral data analysis software to analyze the results as well.

Revise your plan

Along the way, you'll likely discover more actions that you wish to monitor and analyze, such as what people do after clicking on your best voip phone service ad. So keep note of these for the next round of data collection. Data collection is a dynamic process, so it's normal to pick up and drop different aspects of the user's journey to follow, and to update your plan over time.

Analyze the data

 

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You can use user segmentation - the practice of placing users into groups based on shared characteristics (such as region, device used, age, or behavior) - in order to analyze and compartmentalize your data.

A music-streaming app could, for example, compare paying users with non-paying users to see what other characteristics they share and figure out what causes some users to pay for a service but not others. By looking at the user's journey, they could also figure out where the common hiccups lie.

Funnel reports allow you to monitor users' journey from one step to the next (e.g. sign up, followed by clicking on an email link, followed by browsing your website, adding an item to the basket, buying the item, or sharing a promo code). These can also be useful in analyzing your customers' experience in order to optimize it.

You essentially want to use the data to develop customer profiles that are specific enough to feel personalized to the users, but general enough that they can be replicated and applied to multiple users at once. Your data analysis can also help you decide whether you want to outsource any of your work and if so you can research how to find affiliate marketers to find someone to take on the bulk of your marketing work.

Behavioral data analysis takes the guesswork out of your customer service plan

Personalized, targeted user experiences is a win-win setup. Customers largely prefer an experience that is custom-made, and companies can narrow down on strategies that work and optimize sales and customer retention rates.

Ignoring the data, or refusing to collect it, is akin to swinging a baseball bat while wearing a blindfold. You might get lucky and hit the ball from time to time, but this is not a way you would choose to try and win a tournament.

Hopefully, by now you feel a little more confident about using behavioral data to optimize your CX and you won't be searching for "high paying affiliate programs" on Google.

While behavioral data analysis can sound very fancy and difficult, it's quite straightforward when you get into it.

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ABOUT THE AUTHOR

Sam O' Brien - Chief Marketing Officer, Affise

Sam O'Brien 

Sam O'Brien is the Chief Marketing Officer for Affise-a Global SaaS Partner Marketing and Ambassador Program Solution. He is a growth marketing expert with a product management and design background. Sam has a passion for innovation, growth, and marketing technology. Here is his LinkedIn.

Published Monday, December 06, 2021 7:33 AM by David Marshall
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