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Let User Behavior Shape Your Customer Engagement Strategy

SaaS growth relies on customer engagement.

It can seem like an uphill battle garnering your audience’s attention in a competitive market. However, coupled with behavioral data, a personalized experience is one solution to communicating your product’s value.

“Chances are your customers have very diverse backgrounds, interests, and behaviors. A one-size-fits-all approach to customer engagement may alienate your customers and drive them away from your brand,” says Donte Ledbetter, growth producer of content and programming at Appboy.

You can serve customers better by monitoring user behavior. Below are five tactics to get your team moving in the right direction.

1. Identify Where Users Come From

Despite industry misconceptions, customer engagement isn’t a random phenomenon. Consumers don’t land on your website out of thin air.

There’s a reason for their behavior. Whether it’s a referral from friends or a backlink in a blog post, SaaS users arrive to your site with an internal (or external) motivation.

Some consumers possess an actual problem and think your product can offer a solution. Others don’t even recognize their looming challenge, yet find your product somewhat intriguing.

To begin the process of uncovering potential customers’ motivations, you’ll want to examine how they ended up on your site in the first place. These location origins will help your team develop a customer path that fits their interests and needs.

Kissmetrics Analyze makes this possible. The platform offers a chronological flow of user behavior wrapped into customizable reports. You can identify channels and monitor users’ steps in the funnel, from an initial visit to paid signup.

With this insight, you can engage users differently based on how they learn about your brand. You can optimize content and calls to action throughout the customer journey. For example, organic visitors may see a pop-up box to sign up for a product demo sooner than visitors from social media.

How consumers learn about your SaaS product matters. So take the time to analyze the data.

2. Spot User Inactivity

Throughout your career, you’ve learned that business centers around serving the customer. For some companies, this means catering to the constant demands of the consumer.

While this way of thinking works, it’s also important to recognize the actions of your customers. Their behaviors, not necessarily their current desires, provide your SaaS team with insight about purchasing decisions.

For instance, a trial user explicitly tells you she loves your product but never purchases. It can leave your team bewildered. However, if you observe her usage activity during the trial and learn she only tinkered with the application twice, your team is better equipped to ensure conversion.

Product usage data is invaluable for engagement. With the right strategies in place, you go from churning to closing more sales.

Kissmetrics Populations can help you quickly identify trends in user activity and product usage. Here’s a video explaining how it works:

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“The critical barrier to harnessing the potential value in this shift is organizational—companies that learn to design and execute effective customer-engagement strategies will have the advantage; the others will lose ground,” writes Tom French, a director at McKinsey.”

Drops in user activity indicate a break in the customer-brand relationship. Take proactive measures, like sending retention emails or offering one-on-one training, to lure users back to your platform. It’s up to you to earn every user’s attention.

3. Monitor Social Media Interactions

When diving into user behavior, it’s easy to get tunnel vision. You focus entirely on the data that you deem vital for SaaS success.

But like most business functions, customer interactions extend beyond your SaaS product into other channels. For instance, consumers may learn about your product on Facebook and comment on your posts with questions.

Your team doesn’t control these social platforms. Yet, they can become one of the best forms of engagement with your users. They’re already talking with their friends, so it’s convenient to drop your brand a message, too.

Consider these social interactions an investment. A Twitter research study found that “when a customer tweets at a business and receives a response, they are willing to spend 3–20% more on an average priced item from that business in the future.” Plus, customers are 44% more likely to share their experiences.

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Social listening is helpful for determining how to engage your customers. If users begin to complain about a specific product feature, you’ll want to respond quickly and escalate the issue to your product team. Similarly, when users praise your business, you want to acknowledge their compliments and show the comments to your team.

Engaging on social is more about learning from the customer than boasting about your product. Take action on their concerns to increase SaaS growth.

4. Track User Responses to Communications

Communication is key to ensuring strong customer engagement. Your team probably spends hours crafting the perfect message to convey exactly the right tone and details.

That’s commendable. But do you also pay attention to how SaaS users respond to your messages?

From email to live chat to phone support conversations, your users are giving you verbal and nonverbal cues about how they perceive and feel about your brand. User response is pivotal to discovering what makes your customers tick.

Let’s say your Net Promoter Score drops after users talk with your live chat representatives. You should then dive into the language used by customers during the chat.

Are users saying terms, like ‘upset’ or ‘stupid’? Then, you can take a look at when those frustrations occur and for what product issues.

From another angle, you can use current user behavior to trigger how you reply to customer responses. You may send an email about an upcoming product launch. The user opens the message but fails to click a link in the email.

You can track that behavior and follow up with another email or even segment an entire group to receive a different series of messages. Kissmetrics Campaigns gives your team the flexibility to send these types of personalized emails based on user behavior.

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5. Recognize High-Level Product Adoption

According to the Aberdeen Group, best-in-class businesses “achieve an 80% greater customer retention rate” compared to other organizations. It’s because they have “mastered the science (and art) of creating loyal customers.”

Loyalty isn’t reserved only for well-known brands. Microsoft and Airbnb do not own a share of die-hard consumers.

Your SaaS can earn customer loyalty just like the competition. By plotting the behaviors from visitor to power user, you’ll know exactly how to nurture incoming users.

Of course, behaviors will vary depending on your industry, customer type, price point, and product, but there are still specific actions to track.

As a SaaS team, observe how often users log in to your application, the number of times users submit a service ticket, and the usage of advanced features. You’re looking for patterns that indicate a threshold of product adoption above standard users.

These behaviors will prepare your team to send new users down a similar journey. For example, if power users sign in five times a week, you can send an email to new users reminding them to log in to your platform. This way, you’re making a conscious effort to build a habit of loyalty.

Let data move your SaaS business toward more revenue. Past user behavior can produce future customer loyalty.

Engage With User Behavior

It’s time to upgrade your customer engagement. Infusing user behavior into your strategy leads to better experiences.

Start by identifying how users learn about your site. Then, pinpoint gaps in user activity to prevent churn. And always listen to customer feedback to improve the product.

Focus on user behavior. Then, shape your customer engagement.

About the Author: Shayla Price lives at the intersection of digital marketing, technology and social responsibility. Connect with her on Twitter @shaylaprice.


Source: Kiss Metrics

What is Data Quality and How Do You Measure It for Best Results?

Sep 26, 2016
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We’ve talked a lot about data quality in the past – including the cost of bad data. But despite a basic understanding of data quality, many people still don’t quite grasp what exactly is meant by “quality”.

For example, is there a way to measure that quality, and if so, how do you do it? In this article, we’ll be looking to answer those questions and much more. But first…

Dispelling Data Quality Myths

decision-makersThe foundation for ensuring data quality starts when basic requirements are created

One of the biggest myths about data quality is that it has to be completely error-free. With websites and other campaigns collecting so much data, getting zero errors is next to impossible. Instead, the data only needs to conform to the standards that have been set for it. In order to determine what “quality” is, we first need to know three things:

  1. Who creates the requirements
  2. How are the requirements created, and
  3. What degree of latitude do we have in terms of meeting those requirements

Many businesses have a singular “data steward” who understands and sets these requirements, as well as being the person who determines the tolerance levels for errors. If there is no data steward, IT often plays the role in making sure those in charge of the data understand any shortcomings that may affect it.

You Can Have It Good, Fast or Cheap – Pick Two

mcdonalds-junk-food

Everything from collecting the data to making it fit the company’s needs open it up to potential errors. Having data that’s 100% complete and 100% accurate is not only prohibitively expensive, but time consuming and barely nudging the ROI needle.

With so much data coming in, decisions have to be made and quickly. That’s why data quality is very much a delicate balancing act – juggling and judging accuracy and completeness. If it sounds like a tall order to fill, you’ll be glad to know that there is a method to the madness, and the first step is data profiling.

What is Data Profiling?

data-quality

Data profiling involves looking at all the information in your database to determine if it is accurate and/or complete, and what to do with entries that are not. It’s fairly straightforward to, for instance, import a database of products that your company manufactures and make sure all the information is exact, but it’s a different story when you’re importing details about competitor’s products or other related details.

With data profiling, you’re also looking at how accurate the data is. If you’ve launched on 7/1/16, does the system record that as 1916 or 2016? It’s possible that you may even uncover duplicates and other issues in combing through the information you’ve obtained. Profiling the data in this way gives us a starting point – a springboard to jump from in making sure the information we’re using is of the best possible quality.

Determining Data Quality

So now that we have a starting point from which to determine if our information is complete and accurate, the next question becomes – what do we do when we find errors or issues? Typically, you can do one of four things:

  • Accept the Error – If it falls within an acceptable standard (i.e. Main Street instead of Main St) you can decide to accept it and move on to the next entry.
  • Reject the Error – Sometimes, particularly with data imports, the information is so severely damaged or incorrect that it would be better to simply delete the entry altogether than try to correct it.
  • Correct the Error – Misspellings of customer names are a common error that can easily be corrected. If there are variations on a name, you can set one as the “Master” and keep the data consolidated and correct across all the databases.
  • Create a Default Value – If you don’t know the value, it can be better to have something there (unknown or n/a) than nothing at all.

Integrating the Data

When you have the same data across different databases, the opportunity is ripe for errors and duplicates. The first step toward successful integration is seeing where the data is and then combining that data in a way that’s consistent. Here it can be extremely worthwhile to invest in proven data quality and accuracy tools to help coordinate and sync information across databases.

Your Data Quality Checklist

clean-data

Finally, because you’re dealing with so much data across so many different areas, it’s helpful to have a checklist to determine that you’re working with the highest quality of data possible. DAMA UK has created an excellent guide on “data dimensions” that can be used to better get the full picture on how data quality is decided.

Their data quality dimensions include:

Completeness – a percentage of data that includes one or more values. It’s important that critical data (such as customer names, phone numbers, email addresses, etc.) be completed first since completeness doesn’t impact non-critical data that much.

Uniqueness – When measured against other data sets, there is only one entry of its kind.

Timeliness – How much of an impact does date and time have on the data? This could be previous sales, product launches or any information that is relied on over a period of time to be accurate.

Validity – Does the data conform to the respective standards set for it?

Accuracy – How well does the data reflect the real-world person or thing that is identified by it?

Consistency – How well does the data align with a preconceived pattern? Birth dates share a common consistency issue, since in the U.S., the standard is MM/DD/YYYY, whereas in Europe and other areas, the usage of DD/MM/YYYY is standard.

The Big Picture on Data Quality

As you can see, there’s no “one size fits all” approach to maintaining accuracy and completeness on every type of data for every business. And with big data’s appetite for information growing more and more every day, it is becoming more important than ever to tackle data quality issues head-on. Although it can seem overwhelming, it’s worth enlisting data hygiene tools to let computers do what they do best – crunch numbers.

The most important step you can take is simply getting started. The data is always going to grow as more prospects come on board and new markets are discovered, so there’s never going to be a “best time” to tackle data quality issues. Taking the time now to map out what data quality means to your company or organization can create a ripple-effect of improved customer service, a better customer experience, a higher conversion rate and longer customer retention – and those are the kinds of returns on investment that any business will wholeheartedly embrace!

About the Author: Sherice Jacob helps business owners improve website design and increase conversion rates through compelling copywriting, user-friendly design and smart analytics analysis. Learn more at iElectrify.com and download your free web copy tune-up and conversion checklist today!


Source: Kiss Metrics

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