I genuinely love statistics, and I’m getting my Master’s degree in Applied Statistics to prove it! Most people are scared of statisticians because we use lots of weird Greek letters and never can say anything with 100% confidence. However, there are plenty of simple statistical concepts that marketing professionals can and should understand. Here are my top 3 simple statistical concepts that everyone should know:

1. Sample size

Sample size is one of the most basic statistics used to describe a dataset. It is just the size of the sample! As simple as it sounds, sample size is fundamentally important to statisticians because the size of a sample can make or break an analysis. Too small of a sample, and the results may not be significant. Too large of a sample, and the data can become unwieldy and difficult to manage.

In the marketing world, it is always important to be mindful of sample size, especially with all of the recent buzz about “big data” floating around. It seems that data sets are only growing; I will have the opportunity to work with a data set that has upwards of 400 million records. In a situation like this, sampling will come in handy because it will reduce processing time and make for a quicker analysis.

Generally speaking, sampling 10% of the population is a good rule of thumb in statistics.

2. Mean/median

The mean and median are both statistics used to measure the center of a sample. The mean is what most people refer to as the average, and the median is the middle data point (or spot between two middle points if the sample size is even). These two statistics can have similar or different values depending on the distribution or shape of the data. Conversely, knowing the mean and median can allow the distribution of data to be determined if it is unknown.

In the image below, three different distributions are presented: symmetric, right-skewed, and left-skewed. A symmetric distribution has the same mean and median. A right skewed distribution has a greater volume of data toward the left (skewed right refers to the direction of the tail), so the mean is greater than the median. The opposite is true of a left skewed distribution.

In marketing, I have found the mean to be the appropriate measure more times than the median, but that does not mean the median cannot be used. The mean is more sensitive to outliers and influential points, while the median is more robust. That is why the mean gets pulled in the direction of the tail for a skewed distribution. Therefore, when there are many extreme data points, the median should be used instead.

For example, in a traffic by source report (direct, banner ad, search, referral, social networks, etc) one can determine where people are coming to the site from. If there are normally 30 visits from social networks but one day there is a jump to 2,500 visits due to a widely popular post of Facebook, the visits from social network for that day would be considered an outlier. They would falsely inflate the year-to-date mean number of visits from social networks, so using the median would be useful in this case.

3. Correlation/causation

“Correlation, not causation” is a catch phrase commonly used by statisticians. Knowing the difference between correlation and causation is one of the most important concepts in statistics (and life). Many people do not know their proper definitions and often confuse the two.

Correlation occurs when two or more variables have a relationship with each other. For example, height and weight are positively correlated because as height increases, weight generally increases as well. These variables display correlation, but not necessarily causation. Height and weight may seem directly causal, but there are other confounding variables that have an effect on a person’s weight such as health, gender, race, diet, exercise, and so on.

Causation is similar to correlation in that it occurs when two or more variables are related to each other. However, it differs from correlation in that one variable is directly responsible for causing a change in other variables, while with correlation that is not necessarily the case. For example, the greater amount of time you exercise, the more calories you will burn and this is a direct causal relationship. These variables display both correlation and causation.

It is so important not to confuse these two concepts. Unless you know for sure that two variables have a causal relationship, always assume correlation and not causation. For example, a company may launch a new marketing campaign with a lot of banner advertisements and then receive a lot of traffic to their site. However that same week there was also a Facebook post by that company about winning a free prize. While the number of ads and number of visitors to the site are positively correlated, it is not correct to assume that the ads themselves were responsible for increased traffic because the Facebook post is a confounding variable most likely causing the traffic.

With a new understanding of these three statistical concepts, you are now ready to impress your co-workers, peers, and colleagues. At your next networking event, why not strike up a conversation about correlation? Some will chime in, and those who do not will just think you are really smart, so it’s definitely a win-win. Knowledge is power!

MaassMedia works with a variety of clients in a range of industries. If you’d like to know more about how your organization can use marketing analytics to find Transformative Insights™ about your audience, contact us today.

There are many groundhogs in different locations that make predictions, but in Philadelphia we look to Punxsutawney Phil for our forecast. Up to 40,000 people gather to watch Phil each year making Philadelphia home of the largest Groundhog Day celebration. People trust in his predictions even though he only has 39% accuracy, according to StormFax Weather Almanac. Below is a picture of the cute little (not so little) guy helping answer interview questions for Channel 6 News.

How adorable!

Is it possible to predict the outcome of Groundhog Day based on past data? I set out to investigate this by compiling a database of variables that might affect whether or not Phil sees his shadow.

The obvious variables to include were weather factors such as average temperature, humidity, wind speed, dew point, amount of precipitation, sea level pressure, visibility, and heating degree day (the amount of energy needed to heat a building, related to outside temperature). I guessed that these variables will have the most significance.

Other interesting variables I included in the model were day of the week, percent moon visible, whether or not it was a leap year, and the party of the incumbent president. I did not really expect any of these wildcard variables to have a significant contribution, but it was still fun to investigate.

Just for kicks, I threw in a variable I named “number of Bill Murray movies” for each year, to appease those who have seen Groundhog Day and enjoy the undeniable talent of Mr. Murray (myself included).

Method
In order to build a model to predict the outcome of Groundhog Day 2013, first I had to compile my database. I used 40 years of data (1973-2012) and included 13 different explanatory variables. The response variable was whether or not the groundhog saw his shadow.

With this database in place, I was able to start building a model. Since my response variable is binary in nature (two possibilities: yes or no), I have to treat it differently from a regular linear model response variable. This means I will have to round my final output up or down to the nearest integer.

First I created 13 different models each with one of the explanatory variables to find the best one-variable model. I put the most significant variable in the model and then added each of the 12 remaining variables to find the best two-variable model. I repeated this process to find the best three-variable model, however additional variables were not significant therefore the two-variable model was the best.

The explanatory variables that ended up significant in predicting the outcome of Groundhog Day were dew point (in degrees F) and amount of precipitation (in inches) that occurred on that day. Unfortunately, the number of movies that Bill Murray acted in that year was not a significant contribution to the model. Sorry, Bill!

The final model:
Shadow = 1.158317 – 0.014294*Dew Point – 0.728101*Precipitation

Both variables had negative coefficients, indicating that as they increase, the likelihood of the groundhog seeing his shadow decreases. In other words the more precipitation in inches and the higher the dew point, the less likely it is that Phil will see his shadow and the more likely that spring will come early.

In order to predict the outcome of this year’s Groundhog Day, I will use historical temperature data from the past two weeks for the dew point prediction and forecasts from multiple weather sources for the precipitation prediction.

To predict the dew point for Saturday, I used temperature and dew point data from the past 2 weeks. Even though temperature is not included in the final model, it trends similarly to dew point so tomorrow’s temperature prediction will be a good indicator of tomorrow’s dew point. In the graph below, the temperature for tomorrow is predicted to be about 32F, an increase from today’s temperature. Therefore I believe the dew point will increase similarly, so my best guess is that the dew point will be 19F.

According to Accuweather.com, Yahoo! Weather, the Weather Channel, and NBC Philadelphia Weather, there is a low chance of precipitation for Saturday, February 2 (around 0-10%). All sources predict that the precipitation will most likely occur in the evening, so for my purposes I am going to use a precipitation amount of 0.0 inches.

I plugged these two numbers into my final model. If the result is greater than 0.5, then the groundhog will see his shadow. If the result is less than 0.5, the groundhog will not see his shadow. The final calculation is:
Shadow = 1.158317 – 0.014294*19 – 0.728101*0.0
Shadow = 1.158317 – 0.271586=0.886

Since the model output a value of 0.886 which is greater than 0.5 and relatively close to 1, I predict that the groundhog will see his shadow tomorrow. That means 6 more weeks of winter weather! Snow and icy wind lovers rejoice. For the rest of us warm weather folks, don’t put your scarves and gloves away just yet.

At MaassMedia, we apply these statistical techniques to a variety of data from multiple channels. Like our predictions for the Groundhog Day, our analyses identify metrics correlated with success to create predictive models. Knowing what to expect from your audience gives you the insight to develop targeted marketing that optimizes the user experience and boosts ROI. Get in touch with us to learn how your organization can move further along the path to Transformative Insights™.

You cannot manage what you don’t measure, and that certainly applies to marketing. Measuring how well each marketing channel drives traffic to a website is imperative to any company’s success, and analytics allows you to do that. Because my background is predominantly in hospitality, I’d like to demonstrate how analytics can benefit the hotel industry.

55% of all leisure and business reservations are expected to be made online for 2013 (PhoCusWright), which means measuring and optimizing a hotel’s digital marketing is more important than ever before.

Although monitoring standard web metrics like visitors, page views, and average visit duration is useful, that information is just the tip of the iceberg. Truly Transformative Insights™ that change the way your organization manages its marketing spawn from pulling and correlating data, not only from web traffic, but also from a variety of marketing channels.

The average conversion rate for hotel websites is about 2%. In other words, approximately 98% of visitors to a hotel’s website leave without making a reservation. This suggests that the hotel industry could be doing significantly more to capitalize on visitors’ interest. It is imperative that hotels utilize the right data in the right way to paint a clearer picture of how their audience interacts with their marketing.

The key to understanding your audience is segmentation. We’re not necessarily referring to segmenting visitors by age, gender, or geographic location (although, depending on your business goals, those qualities might be important). At MaassMedia, we strive for more granular segmentation to understand each visitor’s intentions, then serve each visitor a unique digital experience optimized for conversion.

With analytics, you can measure where each visitor stands in the purchase consideration spectrum. Some visitors may have their minds made up about booking a room, while others might be on the fence. Defining the Key Website Actions on your hotel’s website and tracking visitors who complete these actions can reflect a visitor’s propensity to buy. Additionally, analytics tracks metrics like how recently or frequently a visitor views the website, which can help identify which visitors are ready to book a room and which are undecided. Using this information, hotel websites can then personalize the content that the visitor sees, raising their likelihood to book.

For example: Let’s say a prospective guest has visited the hotel’s site on numerous occasions in a short time span without making a reservation. We can establish that the guest is probably significantly interested in booking a room, but might need some added incentive to make the decision. Using analytics, the hotel could identify these visitors and display a time-sensitive offer only for those visitors who are on the fence.

Only visitors who need incentive to purchase are shown the time-sensitive offer

Data can also be collected from interactive content, like internal search terms and filters. If a visitor consistently books or views hotels costing over $500/night, the website could recommend more luxurious hotel rooms and properties. Not only would this increase the likelihood of a larger sale, but the purchasing experience for the guest also improves tremendously because they don’t have to filter through the hotels that don’t interest them.

Hotel franchisors can also capitalize on trends in their data. For example, if there is a substantial increase in searches for a particular destination, brands can recommend that their franchisees in that location raise their rates because of the spike in demand. This could substantially improve the revenue that the franchise’s website generates.

Although opportunities to customize or modify the website may be limited for branded hotels (franchisors may not give complete freedom to alter or add content to their website), independent hotels and resorts have significant opportunities to use analytics to their advantage. Even if they can improve their conversion rate from 2% to 2.5%, that’s still a 25% increase in conversion rate.

Many independent hotels haven’t implemented any data analytics on their websites. Without even a basic implementation, hotel owners are missing out on important insights about their clientele. For example, most hotels offer some assortment of packages to their guests, but if hotels aren’t using analytics, they can’t know which packages are most and least popular with certain types of visitors. By tracking purchases only, they miss out on seeing how many guests viewed the package but chose not to purchase it.

Similarly, if the hotel offers services like banquet halls or catering, web analytics can shed light onto visitors’ level of interest and the best ways to market those services online. Are guests searching to sign up for loyalty membership? Do they want to book meetings or events in a particular city or region? Are they more or less price sensitive? Analytics enables hotel brands to gather data that can answer these questions. If utilized correctly, it can be a tremendous asset to improving a revenue management and building a customer relationship management (CRM) system.

Analytics in the hospitality industry provides a way to better understand guests’ purchasing and staying behavior, as well as audience engagement across a variety of marketing channels. MaassMedia works with a variety of clients in a range of industries. If you’d like to know more about how your organization can use marketing analytics to find Transformative Insights™ about your audience, contact us today.

Sometimes, we need to slow things down to find clarity. Step back from the noise and take a look at the Big Picture, not just the Big Data. When you see things from afar, you can discern the kind of information that matters to your business from the information that doesn’t produce actionable insights.

In this post, I will outline my process for developing a marketing plan across multiple channels based on ROI. This is not intended as an “End all, be all” formula. It’s more so a way to get you thinking about your marketing and guide some of decision-making when it comes to your marketing budget.

Step 1: Take a look back

Historical data is an analyst’s best friend. When you know what happened last year, you can easily use that data for future planning purposes. Keep in mind, one data point does not make a trend. You can’t count on that new promotion to keep returning high response rates just because the first campaign was successful. It may have been one and done. I like to use at least three data points for directional purposes, but more are always better.

Step 2: Level the playing field

Although different marketing channels often have very different KPIs, there can be common metrics (Pay attention, because this is where we remove the noise!). With email, you have opens, click throughs, bounces, etc. With direct mail, you might look at metrics like in-home rates and coupon redemptions. You get the point, but to calculate ROI, you’ll need to tie the marketing contact back to a purchase. I know this can be difficult, but once you tie back the purchases you can then use common metrics like response rate, average order, margin % and cost per contact to do comparisons across channels.

Step 3: Plan in chunks

Most businesses are affected by seasonality in some way or another. Just because your mobile campaign performed well over summer doesn’t mean customers will respond the same during the holiday shopping season. Use data from comparable time frames year-over-year. I like to build my plans quarter by quarter, but you may need to use different time periods based on your business strategy.

Step 4: Determine what is driving your marketing plan

This is where things can fall apart. Let me guess, you need more sales and margin, more customers and higher retention, but your budget was cut this year. That might be a problem, but it doesn’t have to set you back. Ask yourself this: Do you need sales today, or can you afford to spend a few dollars on branding? As marketers, we tend to address the immediate needs of the business, often pushing aside the long view. This is where ROI can really help. Take a look at the chart below.

Above are Sales and Margin projections, by channel, based on historical data. Metrics like RR%, average order and margin % are based on prior campaigns, adjusting for seasonality and fluctuations in customer counts. Each channel can have different customer counts according to their nature (i.e. opt-ins for email and mobile). Customers opting in to different channels may have different values (i.e. sale per contact and margin per contact) based on their purchase history, loyalty or a number of other factors.

Step 5: Execution

Let’s say you have $500K to spend for a month-long campaign. Where would you put it?

Here’s what I would do:

Direct mail would get the bulk of the budget because it’s the largest driver of sales and margin per contact, even though it doesn’t generate the highest ROI. Mobile has a great ROI, but there aren’t enough customers yet to drive a month-long campaign. Email could be used to reinforce direct mail or to promote a secondary offer in case the initial offer becomes stale.

Print is not being utilized in this campaign due to its low ROI and low margin per contact. This plan is really looking at the “short run.” The business requires sales now and can’t spend funding on print. Although this might be a tough pill to swallow, your ROI analysis could spur a conversation with higher-ups about branding, which may necessitate a future budget increase.

At MaassMedia, we believe it’s insights like these that are most valuable. If your marketing analysis doesn’t present an opportunity to change what you’re doing for the better, what use is it to you? Find out how MaassMedia can use your business’ data to uncover insights that will ultimately boost your ROI. Contact us today.

TIP: Because this functionality is built into standard GA accounts, you don’t need to change the tracking code on your site to use it.

There are four Real-Time Analytics reports in GA: Overview, Locations, Traffic Sources, and Content. The Overview is more of a dashboard than a report, which displays total active visitors, top referring websites, top social traffic, top keywords, top active pages, top geographic locations, and page views per minute. You can drill down further into your data with the more granular Real-Time reports (Locations, Traffic Sources and Content) and can also apply a segment to these reports if you’d like to filter your data.

TIP: It is important to note that any changes made to a profile can take a few hours to update into the Real Time Analytics report.

Real-Time Analytics can tell you many things about your site. Personally, I use it to ensure the tracking code I have implemented on a site is working properly. Marketers, on the other hand, can use Real-Time Analytics to monitor and react to traffic fluctuations on the fly.

Here are a few ways Real-Time Analytics can uncover Transformative Insights™:

• Measure immediate social media impact. After posting a link to your web content on Facebook or Twitter, check your Real-Time Analytics to monitor traffic coming from your post. This is a great way to learn what content and messaging best resonate with your audience, which social media platform is most responsive, and which day of the week/time of day your audience is most likely to interact with your social media updates. You can apply this technique to other marketing channels, too, including e-mail and paid search campaigns.
• Know when your content goes “viral,” and take action to keep the traffic coming. Let’s say your content gets picked up by a popular website, and visitors start pouring in. Without using Real-Time Analytics, you wouldn’t see your traffic spike until the following day. By then, your content could be old news. Knowing in the moment that your content is hot gives you the opportunity to capitalize on your traffic influx at the right time. You can respond to blog comments to fuel conversation, promote the content on your social media profiles, or optimize the content with links to related content on your site.
• Optimize a short-term promotion. If your business has a limited-time special offer (sale, coupon, giveaway, etc), you can tweak the offer in real time and monitor the effects.

TIP: GA’s Real-Time Analytics also supports Android mobile app tracking and can be used to test the analytics you’ve implemented for your app.

When used for the right reasons, Real-Time Analytics can change the way you see your analytics data. That’s what we’re all about at MaassMedia–seeing things differently. Contact us today to see your data from a new perspective.

Matt Esling is MaassMedia’s Analytics Solution Engineer. In his spare time, he enjoys fishing, reading, watching and discussing sports, and spending quality time with his cats.

If you haven’t segmented your traffic at all, you’re probably feeling pretty overwhelmed by what you see in your analytics platform. Don’t panic–it’s easy to get lost in your data. Breaking down your traffic into chunks of visitors with similar characteristics can not only make your analyses easier, but also open your eyes to insights you wouldn’t see in a pool of data of all your visitors combined.

Think of it this way: Imagine you’re a store manager, and it’s your goal to convince shoppers to make a purchase. After enough experience interacting with the various people who come into your store, you find there isn’t one blanket technique for closing the sale with every shopper. So you start asking targeted questions to uncover each customer’s needs, then suggesting products to meet those needs.

Some customers are looking for something your store doesn’t carry, or for whatever reason they simply can’t be convinced to buy anything at all, no matter how hard you try. You do what you can to help those customers (maybe suggest a different store), but focus on engaging those who are more likely to buy. Over time, your sales increase as you become more familiar with the different types of shoppers and the sales experiences that work most effectively in various situations.

Now take that whole scenario and put it online. The store is your website and your content is the manager who identifies visitors and presents each with a personalized, targeted experience that is optimized for conversion. It’s the ideal retail experience for both the shopper and retailer, and it can continually be optimized as the retailer grows and evolves its business.

The key to success in both the online and offline scenarios is identifying customers and creating a shopping experience that caters to their needs.

Segmenting traffic, targeting visitors, and optimizing content for increased conversion isn’t a simple science, so let’s take a look at the logic behind the process:

Separating Qualified from Unqualified Traffic

Let’s be realistic–Every single visitor on your website isn’t going to convert. You don’t even have a shot with some visitors. Maybe they’re not in your target audience. Maybe they accidentally clicked an ad to your website. Maybe they have their hearts set on buying muffin tops, but your store only sells whole muffins. Whatever the case may be, a portion of your traffic is unqualified for analysis and should be filtered from your data.

To figure out who these visitors are, you’ll need to take a look at the customers who don’t convert and find the qualities they typically share. You might find that these are visitors who don’t click anywhere else on the site (bounces), spend less than one minute on your site, come from an unqualified traffic source, etc.

By filtering the unqualified traffic from your data, you get a more accurate picture of engagement with your target audience. This will help you focus on the traffic that actually holds potential, and your numbers will start to look much more promising.

TIP: Make sure visits from your internal staff aren’t being lumped into your traffic. In any analytics platform, you can filter visits from specific IP addresses.

Segmenting Qualified Visitors

Once you’ve separated the wheat from the chaff, you can divide your qualified traffic into groups of visitors with similar characteristics to observe how each interacts uniquely with your website. If you have an online clothing store, for example, it’s safe to assume that visitors in New York aren’t shopping for swimsuits in January. In that case, your business might benefit from segmenting visitors by geographic location.

You can also measure engagement, and segment visitors based on how apt they are to convert.

Each qualified visitor to your site falls somewhere on a spectrum of conversion likelihood. Some visitors have decided before even visiting your site that they’re going to make a purchase (if that’s how you define conversion). Others might be on the fence. And then there are those visitors we discussed who will simply never convert.

Measuring visitor engagement can reflect conversion likelihood, and grouping visitors together by levels of engagement sets the stage for deeper, more focused analyses.

Before you can measure engagement, you’ll need a list of Key Website Actions (KWAs). These are visitor characteristics and behaviors most closely associated with conversion. It could be watching a product overview video, spending more than X minutes on your site, returning to your site more than X times, viewing current promotions, etc. If you’re not sure what the KPIs are for your site, you’ll need to do an analysis of the website actions that most commonly precede conversion.

Once your KWAs are defined, you’ll need to track each one with custom implementation. From there, you can segment visitors based on how many engagement criteria they meet.

Test, Target and Optimize to Increase Conversion

Okay, so you’ve filtered out your unqualified traffic and segmented your visitors by level of engagement. Now what?

With this implementation in place, you can measure how each group of visitors, ranked by engagement, fluctuates over time and set goals for increasing conversion in each group. However, moving the needle requires some work, and you won’t reach your goals without making changes to your content.

That’s where testing and optimization come into play.

Try making small changes (or, if you’re gutsy, try a large change), then measure how conversion fluctuates within each bucket of visitors. If your conversion rate increased, you made a change for the better. If it decreased, you should probably try something different or revert to the way things were.

You can take your analytics a step further and target visitors within each group with specific content. Maybe you have a group of visitors that consistently visit your site without making a purchase. Presenting that group with a special, time-sensitive promotion could be enough of a nudge to get them to buy.

At MaassMedia, we specialize in deep-dive analyses. We know the most obvious insights are rarely useful, and that traffic must be segmented and analyzed in chunks before an accurate understanding of audience behavior is fully understood. Get in touch with us to discuss what we can do for your organization.

No tablet?

One key difference to keep in mind – more than 95% of tablet users do not have JavaScript enabled. Although these visitors are still tracked in Google Analytics, other JavaScript-based data collection platforms may not be capable of collecting data from visitors browsing on their tablets.

The segment is also an important indicator of the power that iPads now have in driving the convergence between mobile and traditional devices. For B2B sites, especially ones targeting doctors, tablet traffic is approaching 25% of total traffic (It is still in single digits for our consumer sites, but is growing).

And, yes, by tablet we do mean iPad. Google probably named the segment in hopes that someday Android would be a competitor. That day isn’t today. For MaassMedia clients, less than 15% of our tablet traffic is from Androids. The rest is all iPad.

Analyzing traffic by device is just a small part of the big picture. To get a complete view of how your audience is interacting with your organization’s digital media, you’ll need to correlate and aggregate a variety of data from multiple channels.

At MaassMedia, we specialize in developing a holistic understanding visitor behavior. Contact us today to learn more about the Transformative Insights™ we can reveal about your organization’s e-marketing.

Director, Reporting and Data Visualization Practice

Marketing Analytics Reporting Specialist

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It has been shown that the amount of data in the world is growing at a rate of 50 percent each year and more than doubling every two years. Since data volumes are not getting any smaller, it is imperative that organizations attempting to gain knowledge and insights from their data establish a methodology for storing, analyzing, and interpreting big data.

Continue reading MaassMedia’s article, Eight Tips for Working with Big Data, published on WebsiteMagazine.com.

In this post, we’ll take a closer look at what bounce rate really means.

What is a bounce rate?

The bounce rate calculation is simple:

# of single-page visits ÷ total number of visits to the site

Most digital analytics platforms–including Google Analytics, Unica and Adobe SiteCatalyst 15–provide bounce rate by site, page and channel as part of their default metrics. With SiteCatalyst 14, you’ll have to work a little harder to get to the bounce rate, but there are plug-ins available.

As simple as the metric seems to be, it is just as easy to misuse or misunderstand.

When is a bounce a good visit?

Someone leaving your site after viewing only one page isn’t necessarily a bad thing.

Let’s say your site has a prominent offline call to action that encourages people to call in to make a purchase. Your “bounces” could really be completed phone sales.

Similarly, if you have an information site with a landing page that has almost all of the content you want a reader to consume, they could “bounce” and still get your message.

Bounce rates usually go up whenever traffic goes up because, as more visitors come to explore your site, more of them inevitably bounce.

Although bounced visits often stem from dissatisfied visitors, bounce rate still doesn’t measure “good” visits. It really just measures the ratio between good and bad visits.

How do we measure good visits?

A good visit is one where the visitor “got”your marketing message, and this definition varies for each website.

For a branding site, a good visit may simply be a visit that lasts long enough to allow the visitor to learn the brand; for a lead generation site, it may be an email collection; and for an e-commerce site, it may be visitors that put something into a shopping cart (even if they don’t complete the purchase).

Can a rising bounce rate be a good sign?

As long as your site gets valuable, engaged visitors, it doesn’t matter if the bounce rate also goes up.

In the chart above, for example, website traffic increased significantly between February and April due to the introduction of a new marketing campaign. The bounce rate jumps 20 points from 40% to 60%. But the number of engaged, useful visits (in red) also increased, which is a much more important insight to success.

The bottom line is this: As long as the number of new engaged visitors is worth the new campaign investment, the bounce rate isn’t important.

At MaassMedia, we believe that metrics should be meaningful, not misleading.
We’ll work with you to measure engagement based on your organization’s KPIs, using data from multiple sources. Contact us to learn more about how you can better measure engagement to reach insights that transform the way you do business.