The holiday magic was in the air this peak shopping season. Despite industry worries about sluggish sales, more people than ever shopped over the holiday week that includes Thanksgiving, Black Friday, and Cyber Monday. NRF reports that 189.9 million Americans shopped during this time, 14% more than in 2018 (165.8 million). And this year also…
The holiday magic was in the air this peak shopping season. Despite industry worries about sluggish sales, more people than ever shopped over the holiday week that includes Thanksgiving, Black Friday, and Cyber Monday. NRF reports that 189.9 million Americans shopped during this time, 14% more than in 2018 (165.8 million).
And this year also proved that holiday shopping has gone cyber. While Black Friday commanded headlines for decades, online sales are taking over. Early tallies show that the US had the biggest Cyber Monday ever with $9.4 billion in sales. About $3 billion of that spending came from smartphones.
The shopping journey is obviously more complicated than a few days and two channels. Brick-and-mortar store sales on Black Friday dropped 6.2% compared to 2018, according to data from ShopperTrak. However, the firm also notes that foot traffic in stores increased 2.3% on Thanksgiving Day compared with last year. So, while people prefer to buy online, they still like to shop. Similarly, “buy online pickup in store” (aka BOPIS and click-to-collect) also increased for the holiday weekend by 43% over last year, and delivered 64% more value in store than sales from non-BOPIS shoppers.
With so many elements to connect, it’s important for retailers to understand what works and how to give customers a positive experience in the process. Personalization can be a cornerstone of a successful holiday weekend. With a 2x increase in session volume for this year’s holiday shopping weekend over last year, we had plenty of our own data to analyze in order to provide some insights.
There’s a lot of good news for brands. We found a big increase in the use of personalization overall, and as a result, Monetate clients saw improved YoY conversion rates and order values.
Here are a few highlights from our clients’ holiday seasons:
US Conversion Rates were 5.6 times higher for pages with Monetate personalization compared to non-personalized pages.
UK Conversion Rates were 3.2 times higher for pages with Monetate personalization compared to non-personalized pages.
Average Order Value increased 54% for US shoppers exposed to Monetate personalization
Average Order Value increased 11% for UK shoppers exposed to Monetate personalization
Monetate also found that retailers grew the use of personalization by more than 200% from 2018 to 2019 across the US and the UK. This includes growth across segmented experiences, Individual Fit Experiences (one-to-one personalization) and Majority Fit Experiences (dynamic testing).
We also looked at cross-device and customer identification rates (aka the “cold start problem”). Monetate did find that over the Thanksgiving weekend, our clients identified just over one-third (35%) of their visitors across desktop and mobile compared to only 23% visitor identification last year, which is a great improvement. But do the math in reverse and that means 64% of visitors weren’t identified. Our UK clients increased their ID rates but tend to have slightly lower match rates overall due to a different approach to privacy protections.
As we’ve highlighted in the past (see the full post here), using AI-enhanced personalization allows brands to create unique strategies for “small slice” audiences. For example, we found that retained holiday shoppers from a year ago are very loyal and actually spend more even than regular shoppers come next holiday season. This relatively small group are a perfect example of a group that deserves a marketing strategy to identify and personalize a campaign that will keep them coming back next year, growing the group over time.
The good news is that clients can increase their match rates with Monetate and identify more of these small but valuable audiences that are likely to come through over the holiday season. You can collect your Person ID (unique identifier assigned to an individual) either onsite, or through clickthrough. The onsite method can be utilised by using a JS variable or a cookie triggered on sign-up or users log in and / or at checkout.
The clickthrough method uses the query parameter that includes an identifier, set up by your ESP. The value in this parameter is unique and tied directly to a person which means that you can stitch this ID to the Monetate cookie. This will give you the connection you need to bring match rates up and create more one-to-one personalization using IFEs.
We know that holiday planning for 2020 starts now, and it’s the insights we get that help us chart a successful season next year. Now’s the time to analyze what works and chart a course for an even bigger holiday shopping season in 12 months.
Phil Lee is a senior strategist on Monetate’s client success team.
The issue of customer retention comes up in nearly every conversation with colleagues in a service business or software company. As companies aspire to transition to an ongoing customer relationship and recurring revenue model (versus traditional transactional relationships with customers), retaining high-value customers is more important than ever.
After all, depending on what industry you’re in, acquiring a new customer can cost your company up to five times more than retaining an existing one. Moreover, increasing customer retention rates by just 5% can increase profits from 25 to 95%. These factors have helped to give rise to major investments in optimizing customer journeys and customer base marketing, as well as establishing new roles such as “customer success managers.” With so much at stake and so much focus on retention, why then, on average does the typical American business have a yearly churn rate of 15%? Why are so many companies losing customers?
Based on my experience, a major driver is that companies are often targeting the wrong customers to begin with. Year after year, we see companies set themselves up for expensive churn issues by trying to serve anyone and everyone who they can convince to choose their product or service, rather than focusing on their most valuable target customers. In my experience, this type of fire, then aim approach is one of the biggest drivers of churn, negatively impacting Customer Lifetime Value (CLV) and ROI for sales and marketing investments.
My churn story
My first lesson around this came earlier in my career when I was vice president and general manager of T-Mobile’s emerging devices business. At the time, T-Mobile had just launched its 4G high-speed mobile data network. Competitors had gotten a head start in this market, so we were eager to capture a share of this quickly growing space.
My team launched the company’s first 4G capable devices including connected tablets and mobile hotspots. After a few stumbles and months of hard work, we found that we had a winner on our hands. Our sales team across the country rallied around the new product line to pitch to customers, and sales started ramping up – with high double-digit growth each month.
While initial sales were strong, we soon discovered we had a serious issue. Our churn rate, or the percentage of customers that left us, was over 10% per month! At that rate, we would need to replace our entire customer base each year just to tread water. Further, our business model, when factoring in our projected customer acquisition and operating costs, required we retain customers for much longer to maintain positive CLV. We had a serious problem.
As we got to work on solving for the gap, we found several issues. First and foremost, we were targeting the wrong customers. Many of our customers, which included small business owners or entrepreneurs, had purchased our 4G mobile hotspots as a home or small office broadband replacement, rather than its intended use as a mobile solution allowing connectivity on the go. We also had the wrong incentives in place. Sales compensation was heavily weighted toward customer acquisition, not retention.
By identifying the right customers and refocusing our efforts, we were able to turn the corner. We revamped our marketing materials, sales and support training, and even tweaked our compensation incentives to ensure that we were positioning the solution to the right customers for the right purpose. Overall, our sales slowed, but churn rate also dramatically decreased. By refocusing our brand strategy, we were more profitable and driving more value for the company – with fewer customers.
The customer retention challenge with T-Mobile is not an uncommon story. In my experience as a consultant, customer churn – driven by a lack of definition or focus on target customers – is a widespread problem. It also explains why on average companies waste more than 25% of their marketing spend on the wrong strategies and channels.
With that overview in mind, here are three key areas companies need to focus on to avoid customer churn:
1. Put your Most Valuable Customers at the center of your brand strategy
Regardless of what industry you work in, serving and over-serving your most valuable customers is the single most important factor in any company’s success. Your MVCs should be the center of gravity for your entire brand strategy – from the products and services you bring to market to your marketing and sales efforts to reach them.
Often when there’s a spike in customer churn, we see companies react and look for a culprit. Comments you may hear include: “The product team isn’t doing enough to improve CX.” “The marketing team isn’t doing enough to retain customers or generate quality leads for sales,” etc. While reacting or looking for a culprit to blame is human nature, this can often lead to short-sighted and siloed efforts by the product, sales and marketing teams.
To be successful in today’s market, everyone across the organization needs to align around a common definition of who the target customer is, what their needs are and what motivates them. You need a single version of the truth around what customers you’re targeting as well as what customers are bad for business. Key questions marketing leaders needs to ask:
Have we done the upfront segmentation work needed to truly know who our MVCs are?
Do we have the right tools in place, customer insights and data to allow the product, marketing and sales teams to identify, listen to and effectively target those customers?
What type of customers should we stay away from?
Leading brands develop a 360-degree view of their MVCs, who they are and how, where and what they buy and why. Identifying your most valuable customers also forces you to make important choices and prioritizations across the business – from your overarching brand strategy to product development, marketing, pricing and customer support.
2. Size your product and experience to the customer
Whether your company is a service provider with a recurring revenue model, or one with a large portfolio of products, you need to know exactly what features, experiences, pricing and messaging are going to resonate with your MVCs or priority segments. Day in and day out, you need to put the voice and experience of your target customers (not all customers) at the center of your product development and CX efforts.
This type of design thinking is absolutely critical for any company to succeed long term. It’s also vital that marketing has a strategic seat at the table with product development team to apply market insights and feedback from target customers to shape future products and CX improvements.
Resist acquiring customers using steep discounts, or getting them in the door by offering a “minimal viable product” with a reduced feature set that will likely not fit their needs. Rather, look to “right-fit” the customer with the product or service option that best meets their needs. That approach is far more likely to build satisfaction and loyalty over time.
3. Embrace experimentation
Customers today expect brands to anticipate their needs in the moment – from their personal shopping preferences to surfacing the right content and experience based on how, where and when they engage. Doing this well requires a cross-company effort to apply both better data and analytics and qualitative insights and voice of the customer feedback to anticipate what your high-value customers want, and in turn surprise and delight them, improving acquisition and retention.
Through experimentation and applying better data and analytics, brands are able to implement strategies to better retain and acquire your MVCs. Key questions leaders need to explore include:
What are low-cost or low-risk ways to encourage trial of a product or service to ensure the right “fit” before a customer makes a purchase decision?
How do we design our marketing and sales process, onboarding, and overall user experience to ensure the right customer is matched with the right product or service?
How can we better tailor our brand positioning, messaging and CX to spark interest and loyalty with the right customers, encouraging word of mouth referrals?
Why this all matters
By investing in the right marketing and sales strategy up-front, identifying the right, high-value customers to target, and optimizing for those specific customer journeys, companies can drive far stronger customer growth, retention and brand equity over time. But all of this only works if you’re focused on the right customers in the first place.
Eye tracking enables business owners and marketers to understand user interaction with their websites and landing pages. They can draw a lot of startling insights by using eye tracking with heat maps, and strategize the design of your landing pages. Are you struggling to meet the desired business goals and want to consider eye tracking…
Eye tracking enables business owners and marketers to understand user interaction with their websites and landing pages. They can draw a lot of startling insights by using eye tracking with heat maps, and strategize the design of your landing pages.
Are you struggling to meet the desired business goals and want to consider eye tracking to peep into your user’s mind to uncover why? If yes, please read on.
What is Eye Tracking?
Eye tracking is the process of measuring and analyzing patterns of visual attention of your prospects when they land on your website. Fixation of eye movements is the typical metric of an eye-tracking system.
When collated over a period of time, the data can provide you with crucial insights, such as where on a web page or other pieces of digital content a visitor has looked and paying most of their attention. Analytical tools like heatmaps serve very handy when it comes to mapping eye movements.
What Eye Tracking Can Do to Your Website Design?
Eye tracking can provide you with valuable insights, such as:
Where your site visitors are looking and for how long are they looking
How did their focus move from one item to another on your web page
What parts of the user interface do they miss
How are they navigating through a particular page
How the size and placement of various page items are affecting their attention
Having said that, below mentioned are ten smart ways to use eye tracking to enhance your site’s design and improve conversions.
1. ‘Fold’ Isn’t as Important as You Think
People do scroll. You just need to give them the right design cues to prompt their curiosity and move beyond the first page fold.
For instance, many marketers argue that placing your call-to-action(s) above the fold is always a better option as the chances of your visitors easily identifying and clicking on them are comparatively high. Several case studies prove otherwise. They’ve concluded that it typically depends upon your visitor’s motivation.
If, after looking at your audience’s eye movements, you think that placing your call-to-action on the left side of the page is not getting you the number of clicks as anticipated, change its position. Develop a hypothesis and run an A/B test. Rule out the guessing game and use actual data.
2. Visuals Attract Instantly
Our brains are far more engaged by storytelling, especially when they’re accompanied by images and videos than heavy text placed all over the page. The reason being, people are more drawn towards visuals as they enrich their experience.
See Google search results below from Moz’s eye-tracking study:
Clearly, results with video thumbnails are getting more attention than textual results.
Your prospects tend to look first at the image and then read on the text if the visuals are captivating enough. With this human behavior, the ball lies in your court as it can facilitate the rise in conversion rates by fast tracking the decision-making process of your prospects.
Formerly, the image on their landing page showed a laptop screen to draw attention to the announcement of the conference, giving a false impression that the event was a virtual conference instead of a live event.
However, their CTR shot to 40%, when they replaced the laptop screen with an image of a conference as shown in their variation below confirming that relevant images play a crucial role in pushing visitors down the conversion funnel:
3. Apply the Contrast Principle
Before-after examples allow easy comparison and force people to pay attention to everything you intend to bring to their notice.
Robert Stevens of ThinkEyeTracking.com experimented to confirm this behavior in real life. The first group of people was shown only the promotional items.
The second group of people was shown promotional items stacked with full-price items. The eye-tracking study showed that these believed-to-be useless ‘pre-sale’ prices were not that useless after all.
Consumers from the second group took note of the full-price of items during the purchase. They were more satisfied with getting a good value for money rather than their counterpart, which was shown promotional items only.
4. Adapt to F-shaped Reading Pattern
It’s a human tendency to begin reading from the left side of the page and move towards the right. F-shaped reading pattern authenticates the same.
As a visitor lands on a page, they automatically pay attention to the elements placed on the left side of the page than the right ones and that follows even when they move further down.
They shifted important testimonials from the right side of the page to the left side so that they’re prominently visible before the call-to-action. As hypothesized, the testimonials influenced prospects’ thought sequence besides enhancing their user experience, helping them increase their sales as anticipated.
Similarly, Baby Age website challenged the standard eCommerce design convention of keeping call-to-action (CTA) buttons on the right. When they switched the CTA to the left, they got a 16% sales boost.
F-shaped pattern also suggests that a website’s header gets a lot of attention. You should put information, such as free shipping, contact number, search bar, money-back guarantee, et al. in a strategic position to increase visibility and the website’s conversion rate.
5. Guide Them with Directional Cues
Human eyes tend to follow the direction they’re pointed in. As the call-to-action is your most important page element, you must point your prospects towards it.
Notice in the image given below how the arrow brings attention to the search bar immediately, making the purpose of the page very clear for prospects:
However, using a pointer is not the only way to guide an individual’s attention to a particular direction. With images, it gets more subtle than that. See this walking path in the image give below:
Didn’t you naturally look where the path is leading in a quick first glance? That’s because the defined path is guiding your eye’s movement.
Similarly, eye-tracking studies have shown that it matters where subjects in images are looking. Subconsciously, people tend to follow the gaze of subjects and look in the same direction. This is illustrated in the image below, taken from an eye-tracking study:
Make your subject look or point in the direction of your call-to-action (or important information you’re trying to convey) and test it to see how it impacts your conversion rate.
ConversionXL conducted a research on which visual cues drive the most attention. They created variations of a lead gen page featuring different visual cues.
The variations had one of the cues each from the following:
Human looking away from the form
Human looking towards the form
The following graph shows the results of how different cues impacted the average time of users looking at the form. Clearly, the variation showing an arrow pointing towards the form won.
6. Don’t Make Them Dwell on the “Dead Weight”
The Fitt’s lawprinciple states that an element’s ‘weight’ in the visual hierarchy determines the attention it gets. Your call-to-action should ideally have the highest weight on the page. But if a less critical, non-clickable element carries the weight that diverts visitors from the call-to-action, you must take measures to fix the visual hierarchy.
A great example here is TechWyse’s case study. See their original page below followed by its heat map:
Heat map of the above landing page:
The ‘No-Fee’ badge is attracting maximum attention on the page. But the problem is that it is a non-clickable element and hence, stealing away the thunder of the main call-to-action button. Removing the badge fixed the flaw in the visual hierarchy of the page, allowing the call-to-action button to get the attention it deserved:
7. Use Whitespace Wisely
Any space that is free from images or text is whitespace, no matter what color it might have. Appropriate use of whitespace increases legibility and allows natural eye flow on the page. As a result, essential page elements get the necessary traction and improve the chances of more conversions.
Placing call-to-action and headline on whitespace helps to make them stand out on the page. When you use a larger-than-life image as the backdrop, it serves as a perfect whitespace to reel people in.
Square Space pulls this off nicely:
Whitespace is extremely important to improve readability as well.
Their variation that lifted their conversion to 10% applied an efficient buy box strategy, as shown below:
8. Tune Your Typography
How you style or present your text is what makes people decide whether or not they’d want to explore and engage with your site. Crammed text like the one shown in the image above will dissuade people from reading it. But only taking care of text spacing isn’t enough either.
Headings and subheadings must stand out to adapt to online scan behavior. Give them relevant h1, h2 tags. Use short paragraphs and sentences, and a font style and size that’s easy to read. See how you can easily spot headline and subheads in the squint test below in the left image:
Bullet points in the text also come handy when it comes to giving a quick overview to readers about the important points. Sometimes knowing what to emphasize can make a big difference.
Their control looked like as shown below with the sale price mentioned as standard text which was not standing out from the rest of the content:
Their variation, as shown below, had the price emphasized in the bold font that resulted in a massive rise in CTR as well as in revenue:
9. Encapsulate What’s Important
The foundation of good visual hierarchy is based on prioritizing your website goals that are aligned with your business goals. Call-to-action buttons, lead-generation forms, or even some important points listed in a box can all work really well for your conversions.
Frames draw eyes to what’s inside them. For example, Ozscopes rests buyer anxiety by addressing their main concerns in a neatly-designed box that cannot be missed on their product pages. Check out the image given below:
All the above points are necessary to understand how you should guide visitors’ eyes on your webpage for better conversions.
However, you must do your research and ensure that your business goals are well-aligned with your expectations from the eye tracking tool, before finalizing it as a potential website marketing strategy for your enterprise.
Spotify has rolled out a new suite of marketing tools for podcast advertisers, making it possible to view impressions, frequency, reach and audience demographic information for podcast ads, the company announced Wednesday.
The new metrics tools are supported by Spotify’s new podcast ad technology, Streaming Ad Insertion (SAI), which utilizes data from the platform’s logged-in audience in tandem with its streaming audio service.
New insights. Spotify is offering podcast publishers and advertisers data on real-time ad impressions, reach (the number of unique listeners who heard an ad), frequency (the number of times a listener heard an ad) and anonymized audience information such as age, gender, device type and listening preferences.
The anonymized demographic data first arrived for podcast publishers in August, 2019.
Why the data is available now. Spotify requires users to log in, providing the company with basic demographic data on the listener. The shift in audience preferences from downloading episodes via RSS feeds to streaming them has enabled companies to extract more information on listening habits.
Why we care. The podcast sector has grown by leaps and bounds, but for all its popularity, the lack of targeting and reporting data has kept many brands from investing in podcast ads.
Now that advertisers and publishers can get more precise information on whether their ads are getting listened to, who and how many people are listening to them, the medium is more transparent and more likely to attract new advertisers, which may help to continue the sector’s momentum.
Kaggle, the Google-acquired data science platform, started as a virtual meeting point for machine-learning geeks to compete on predictive accuracy scores. It evolved into a Swiss Army knife for data science and analytics—one that can help data professionals, including data-driven marketers, elevate their analytics game. Despite being a free service, Kaggle can help address an […]
This is, of course, just a partial list. This post focuses on these and other marketing-friendly use cases for Kaggle.
What is Kaggle?
Kaggle launched in 2010. It became known as a platform for hosting machine-learning competitions. The competitions were typically sponsored by large companies, governments, and research institutes.
Their goal was (and still is) to leverage the collective intelligence of thousands of data scientists around the world to solve a data problem.
In 2017, Kaggle was acquired by Google. After the acquisition, it started branching out into more areas of data science and analytics. The aim is clear—to become a one-stop shop for data professionals. (It’s currently being rebranded as “the home for data science.”)
Below, I discuss five fresh and relevant features for marketers, regardless of technical ability:
Have you ever been in the following situation? You’re gazing over a large data file with lots of numbers but little explanation. You’re trying to figure out what each row and column represent, and no one seems to have precise documentation.
What if we could ensure our datasets were clearly documented? This goes beyond just having a data dictionary for feature definitions.
What if we knew who collected the data, the sources and methodology they used, and if any data is missing? And, if so, why? Is it random? Is there a pattern or reason behind it? Wouldn’t it be nice to know, too, if someone, somewhere, is actively maintaining the dataset?
This is the idea behind Kaggle datasets, a collection of thousands of high-quality datasets—all with an automatic quality score based on availability of metadata. These datasets are searchable and have helpful tags attached to them (e.g., industry, data type, associated analyses, etc.)
Where applicable, the data sources are verified, too. And there’s an added bonus: Given an initial dataset, Kaggle can make recommendations for relevant, complementary datasets.
There are more than 20,000 datasets in Kaggle, including census, employment, and geographic data, which analysts can access and analyze directly from their browsers. Most importantly, there’s a large variety of datasets related to marketing, ecommerce, and sales.
How do you find datasets on Kaggle?
It couldn’t be easier:
Connect to kaggle.com. (There’s an optional Google login.)
Look for the datasets section near the top of the page.
Enter a keyword to search the datasets database.
Scan the results, review the dataset quality scores, interestingness scores, and short descriptions.
Select the dataset that resonates most with you.
Bonus dataset: Google Analytics data from the Google Merchandise store
Digital analysts can access raw, hit-level data (with full ecommerce implementation) that spans a full year of customer activity in the Google Merchandise store.
Working with this dataset can be valuable in terms of understanding the underlying structure of Google Analytics data and experimenting with a number of advanced statistical and data mining techniques that can’t be applied when the data is in aggregate form (which is the norm with standard Google Analytics.)
2. Kaggle community analyses: Jump start your analysis by reviewing others’ work.
When starting to analyze your marketing data, finding relevant datasets to combine with your original one is useful. But it’s even better if you can see all existing work that’s been published on a given dataset by other Kagglers. This can be a source of inspiration but also a time saver, especially in the initial stage of an analysis.
It’s sometimes daunting to choose among all available analyses. Similar to a social network, Kaggle shows you how the community has interacted with each piece of work, which can help you spot ideas and analyses that stand out. It’s also a good opportunity to interact and network with members of the Kaggle community who have overlapping interests.
A good example of this is the Google Analytics dataset from the previous section. It’s accompanied by hundreds of approaches on how to analyze digital analytics data from the Kaggle community—including some from Kaggle grandmasters.
How do you find relevant marketing analyses on Kaggle?
After selecting a dataset as described in the previous step, you’ll notice that there are several independent Notebooks associated with it. (Notebooks are discussed below in more detail.)
Every Notebook represents an analysis that includes narrative, code, and output, such as visualizations and data tables with summary statistics.
To get started, select the one with the highest number of upvotes, a sign of quality and approval from the community.
If the analysis is indeed of high interest, it’s possible to “fork” the Notebook, thus generating a copy of both the code and data.
Then, either run the script as is or make changes by creating your own version. An interesting option is to substitute the original author’s data with your own similar dataset before executing the code.
3. Kaggle Notebooks: Access a powerful laptop on the cloud.
By now, you’ve selected a dataset and collected some good ideas from the Kaggle community to help you get started. As a next step, you’ll want to apply this to your own data.
What’s the most suitable place for all this to happen? An obvious option is your local desktop or laptop. Alternatively, you can go the Kaggle way by working with Kaggle Notebooks (previously known as Kaggle Kernels). This has benefits, especially in cases when:
The dataset is several gigabytes in size and impractical to move around or load into local memory every time you analyze it.
The task is computationally intensive, and you don’t want to slow down your laptop for the rest of the day.
You’re planning to share your analysis with collaborators.
Let’s have a closer look.
Notebooks and computation
A Kaggle Notebook is essentially a powerful computer that Kaggle lets you access in the cloud. It used to be available only for use with public data during competitions. Recently, Kaggle started offering it for private projects at no cost and with the option to use private datasets.
Visually, Kaggle Notebooks look like Jupyter Notebooks, containing computation, code, and narrative—but they come with some nice extras:
They’re equipped with processing hardware, CPUs and GPUs, for computationally demanding analyses. This processing power is useful if you have a lengthy computation or expect a high volume of data to be returned after an API call.
They have RAM memory of 16 gigabytes, which can be used to fit large datasets into memory. (This is more capacity compared to the average laptop.)
You can attach one or more datasets to a Notebook in a single click, with a total size of up to 100 gigabytes.
Notebooks and collaboration
You can share your analyses with colleagues—without the dreaded “but it works on my machine” scenario. When you share a private Notebook with your collaborators, they automatically access the same isolated computational environment, including the software libraries and version of the programming languages.
Thanks to Docker, the popular containerization technology, there’s no need to install or update software, and no risk of causing software conflicts.
As soon as your work is done, select public or private visibility for the notebook and share it with collaborators. They can view and run the analysis interactively with one click, straight from their browser.
4. Kaggle cloud integrations: Get access to Google Cloud tech.
Working within the Kaggle environment acquaints you with cloud workflows. It also offers exposure to new tools and tech—opportunities to pick up new skills, many of which are vital to marketers and digital analysts.
I won’t discuss these integrations in great detail here—CXL has several sources (linked above) with detailed product walkthroughs. When it comes to how this works with Kaggle, the essence is that you can:
Access data stored in BigQuery directly via Kaggle with some SQL code, then analyze it directly on Kaggle with R or Python.
Build and evaluate regression and clustering models without extensive knowledge of machine-learning frameworks.
Load a dataset in Kaggle, shape it, and then—via the Data Studio connector—explore the data visually in the Data Studio interface or create dashboards to share with your team.
There’s also an integration with Google Sheets and a brand new one with Google AutoML (see the next section). I wouldn’t be surprised to see more integrations since Kaggle is now part of Google Cloud.
5. Machine learning with Kaggle: High-quality machine learning and AI with zero code.
Integration with Google’s AutoML was announced in November 2019. It deserves a section of its own because of its potential impact.
As a concept, AutoML isn’t entirely new, but making it accessible as a product en masse via Kaggle is a noteworthy development. The human expertise that’s required for machine-learning development is scarce, a fact often brought up as a bottleneck for the field.
AutoML can lower the barrier to entry for development of machine-learning applications in marketing. It allows marketers with a general understanding of the machine-learning process to use advanced, powerful AI models safely—and without needing to be programmers.
AutoML, which is now available on Kaggle, can also save massive amounts of time spent developing and testing a model manually (the typical case right now).
This won’t, of course, be “AI at the push of a button.” The marketer (or whoever applies AutoML) will need to understand the basics of the process. Unlike other features in Kaggle, its use may result in costs for computation.
Kaggle doesn’t cover all aspects of a data and analytics workflow. It’s not the tool to develop production-level systems or store and manage all of your analysis code and artifacts. However, it’s a practical collaboration tool with which marketers can access relevant datasets, explore data, and get ideas to jumpstart their analysis.
Computationally, it’s like a powerful, cloud-based laptop that’s always available for public or private projects. It’s also a bridge to many other cloud services provided by Google, such as BigQuery and Google Data Studio.
Last but not least, AutoML has the potential to be a game changer. It lowers the barrier to entry and empowers marketers to get directly involved in the development of AI and machine learning for projects.
Becoming familiar with Kaggle Notebooks, the Cloud integrations, and all the other elements of the Kaggle environment can make a future transition to a full-fledged AI platform, including Google’s AI platform, much easier.
As we’ve previously reported, the California Consumer Privacy Act (CCPA) has an overarching objective to give consumers control over their personal information, yet it looms as the first general consumer privacy law that will affect all domestic US ind…
As we’ve previously reported, the California Consumer Privacy Act (CCPA) has an overarching objective to give consumers control over their personal information, yet it looms as the first general consumer privacy law that will affect all domestic US industries, including healthcare. In the continued absence of any all-encompassing federal legislation, the CCPA will set a new standard for treatment of consumer information.
What is Heatmap? Heatmap can be defined as a method of graphically representing numerical data where individual data points contained in the matrix are represented using different colors. The colors in the heatmap can denote the frequency of an event, the performance of various metrics in the data set, and so on. Different color schemes are…
Heatmap can be defined as a method of graphically representing numerical data where individual data points contained in the matrix are represented using different colors. The colors in the heatmap can denote the frequency of an event, the performance of various metrics in the data set, and so on. Different color schemes are selected by varying businesses to present the data they want to be plotted on a heatmap. Some like eCommerce marketers use the hot-to-cold color scheme whilst others like stock market analysts use the cold-to-hot color scheme. The benefit that every heatmap user enjoys, irrespective of the industry, study field, and purpose, is the simplification of complex numerical data through visualization that can be gauged at a glance.
The History and Evolution of Heatmap
Heatmaps have been around since the 1800s and have since evolved into what they are today. Having originated in the 2D display of data value in a matrix, where dark grey or black blocks represented larger values and lighter shades represented smaller values, heatmaps have now taken a very unique form. The first known usage of heatmaps is credited to Loua in 1873 for presenting various social statistics in Paris using colors:
Loua’s heatmap used dark grey and black to denote higher value metrics and light colors like white and light grey to denote lower value metrics. Other instances of heatmap usage have been earmarked by authors like Brinton, who used heatmaps to present educational data in 1914. Gower & Digby (1981), and Chen (2002) developed a new type of heatmap that represented both the recorded data and the diagonal similarity between matrices by attaching dendrograms. The most recent development in the evolution of heatmaps has been the incorporation of hover and click data into the toolbox.
Today, heatmaps have become so versatile that they have become the go-to tool for data visualization and analysis not only for statisticians but marketers, business owners, biologists, geographers, and the like. It has taken multiple forms that differ from each other based on their usage in different industries for varied purposes.
What is Heatmap Used For in Different Industries?
Let’s take a look at how heatmaps are used by professionals across industry and how each of them benefits from its usage:
Stock Index Heatmap
In the financial market, stock index heatmap helps identify prevailing trends in the market at a glance. It uses a cold-to-hot color scheme to indicate which stock options are bullish and which ones are bearish. The former is represented using the color green, whilst the latter is highlighted in red.
Geographical heatmap or geo heatmap represents areas of high and low density of a certain parameter ( for instance, population density, network density, etc.) by displaying data points on a real map in a visually interactive manner. Industries like real estate, travel, food, and so on can greatly benefit from the usage of geographical heatmaps.
Real estate firms can map out all the properties they offer and identify opportunities to divert more resources towards high-profile neighborhoods. Travel websites make use of geo heatmaps to represent the most happening and busy spots in the selected destination to help travelers make an informed itinerary depending on the kind of vacation they are planning.
Food entrepreneurs, on the other hand, can create a geo heatmap to identify markets where there is the least amount of competition or to identify markets which have not already been swamped by rival food joint and chains.
Website heatmap represents the hottest (most popular) and coldest (least popular) sections of your web pages using a hot-to-cold color scheme with the warm-toned colors depicting the most popular sections and cool-toned colors depicting the unpopular ones. Website heatmaps are of utmost importance to organizations that have a strong online presence and use the internet as their main revenue channel like eCommerce stores, travel and hospitality websites, OTT media services, B2B SaaS companies, and so on.
Using website heatmaps, businesses can track user behavior and discover actionable insights that help them measure their website’s performance, simplify numerical data, read their visitors’ minds, identify friction areas by identifying dead clicks, redundant links and so on, and ultimately make changes that positively impact their website’s conversion rates.
Heatmap in Sports
Sports heatmap is actually a very fascinating use case for heatmaps. By plotting heatmaps of players’ on-field performance, coaches and managers can identify their game pattern, performance areas that need improvement, study their rival’s possible game plan as well as strategy, and make data-backed decisions that not only benefit the players but the entire team and ultimately their business and turnovers.
Statisticians and analysts employ a plethora of tools and methods to sort the collected data and present them in a more user-friendly manner. To this end, heatmaps help professionals from every industry. To sum up, the reason why heatmaps have gained the impetus they have in the past few decades as a statistical and analytical tool is that:
It is visual and is an intriguing method of data representation
It is readily and easily consumable as it simplifies numeric data and depicts them using a color scale
One can easily compare various data points plotted on different heatmaps
It is versatile and adaptable as it can record and present both absolute and derived values
It removes multiple steps from traditional data analysis and interpretation process by laying down all the values in one single heatmap
Having trouble viewing the text? You can always read the original article here: A Behavioral Design Framework for You and Your Website
Maybe the best behavioral design framework for your website is the same one that you can use to change your personal …
Maybe the best behavioral design framework for your website is the same one that you can use to change your personal habits. The man walked onto the stage in a colorful robe. He was holding a small oar. He claimed he was wearing a magician’s robe and that the oar was his magic wand and […]
Over the past decade, marketers have heavily advocated the use of contextual targeting (placing ads where “best-fit” potential customers are likely to browse) to get more visitors aboard and convert them into buyers with a subsequent repeat purchase. But with the advent of web-tracking technology, behavioral targeting has overridden contextual targeting by the margin. In…
Over the past decade, marketers have heavily advocated the use of contextual targeting (placing ads where “best-fit” potential customers are likely to browse) to get more visitors aboard and convert them into buyers with a subsequent repeat purchase. But with the advent of web-tracking technology, behavioral targeting has overridden contextual targeting by the margin.
In addition to segmenting visitors basis their web browsing behavior, behavioral targeting helps achieve higher engagement in an age and day where online swimmers are developing strong avoidance towards irrelevant ad formats.
Furthermore, when used alongside personalization, behavioral targeting can help (and has helped) businesses scale up at an exponential pace. Needless to say, that market giants like Amazon, Netflix, and Booking are live examples of companies heavily using behavioral targeting to amp up their engagement and conversion rate.
Let’s understand how to leverage behavioral targeting to your advantage.
The Behavioral Targeting Process
1. Data Collection and Analysis
Visitor behavioral targeting data is typically gathered from multiple sources, including websites, mobile apps, CRM systems, and many other marketing automation systems. This data further comprises of an individual’s:
Login information (in case of registered users) such as frequency of logins, particular hours of logins, number of devices used to login, and so on.
Sites/Pages users visit on the site
Depth of content engagement
Last engagement date and items purchased
Page real estate
While gathering such data seems like a comparatively simpler task, many special types of tracking pixels or third-party cookies are employed to do the needful. Once collected, the data is then used to analyze and create audience segments.
Using data management platforms also serves beneficial. For a reason that they don’t just allow you to harvest data about your target audience such as looking at a consumer’s offline information and other data their device(s) gathers on a regular basis, but also store all the information, analyze it, and use it for successful behavioral targeting.
Basis segmentation, unique campaigns are now designed and implemented, at this stage, to match the needs and requirements of specific audience segments and make every advertisement more relevant to them. This not only helps add a personalized touch to your campaigns but increases the chances of more responses and conversions as well.
However, setting up campaigns is just the onset. Measuring and validating the performance of your personas and targeted content is equally essential. Using qualitative and quantitative tools such as analytics, heatmaps, scrollmaps, and visitor behavior analysis, as well as A/B testing your campaigns on specific segments to understand whether or not you’re moving the right direction, is crucial.
Behavioral Targeting and Personalization
More than a luxury (or an added advantage) website personalization is now hygiene helping businesses flourish in the era of stiff competition. Treating each website visitor as a separate and unique entity, and engaging with them in a more complex interaction than the conventional one-site-fits-all strategy has become paramount. So, by interpreting a visitor’s intention in real-time using behavioral targeting, it becomes possible to respond to each intention in a personalized manner.
Here are some personalization techniques where behavioral targeting plays a prominent role:
Recommendation engines that show products/services basis an visitor’s past content consumption: Youtube is a great example to quote here. The video-sharing platform, through its personalized recommendation engine, provides multiple video recommendations to its users along side showing relevant ads.
Targeted email campaigns based on the behavior of a user: Amazon masters this domain. If a user pays a visit to the eCommerce giant’s platform, browses through some of its smartphone categories, but leaves without making any purchase, Amazon instantly sends a personalized email (check the image below) to the user sharing a list of similar items that might be of interest to them.
Dynamic landing pages that highlight more relevant content to a user/visitor: When executed well, these pages have the prowess to get more business leads than many other website pages. For a reason that they not only speak directly to each individual’s specific needs but give them something to stay on your site and even take the desired action. Below given is a perfect example of a personalized landing page.
Benefits of Behavioral Targeting
Besides being a good marketing technique, the advantages of using behavioral targeting are much more tangible than abstract number systems. Creating campaigns using visitor behavior data not only benefits business, but visitors themselves can reap multiple rewards as well. Below mentioned are some advantages of behavioral advertising:
Increase On-site Engagement
Behavioral targeting serves as one of the best techniques to re-target and/or re-engage with visitors that abandoned your site due to some or other reason(s). By putting together the missing pieces of information about a visitor’s on-site journey such as, the products/services they showcased their interest in but left without buying, and other similar information, you can retarget them by showing content (messages, products/services, etc.) that reinstate the closed conversion process.
For instance, Flipkart uses emails to to re-target and re-engage its customers and get them to enter their conversion funnel.
Initiate Long-lasting Communications
With behavioral targeting at hand, the possibilities of engaging with all sorts of visitors segments are endless. For instance, if you’re planning to target visitors from Amsterdam, then create special newsletters for visitors hailing from this specific region. Let them take a peek into your newsletter to instill trust, and then invite them to subscribe to the newsletter services. Such a method not only helps initiate long-lasting communication but also get more visitors aboard your lead-gen ship.
Hook Indecisive Prospects
While swimming through your site, some visitors may display behaviors suggesting they’re interested in your offering, but at the same time, they also have doubts about whether or not to proceed. If that’s the case, then push these indecisive prospects a little hard. Bait them with some incentives or discounts, and even show personalized recommendations for them to be more conclusive in their decision. Here’s an example for you to understand.
Note that “indecisive behavior” can be identified by the number of pages a visitor views, their time spent on the site, and many other similar patterns of search. It’s typically up to you to settle on metrics you wish to analyze according to your site’s profile and target to define and segment indecisive prospects into one bucket.
Increase Average Order Value
Every time a visitor explores your website, they leave unique navigation pattern. It is this pattern that proves beneficial and helps recommend products/services that correspond to the visitor’s preference. By helping your on-site visitors discover similar and interesting products/services can not only help increase the number of orders you receive but their overall value as well.
To conclude, behavioral targeting is a powerful tool that creates an opportunity for businesses to understand better and meet the needs of their target audience while achieving their business goals and increasing ROI.
What Is User Research? Usability.gov defines user research as “understanding user behaviour, needs, and motivations through observation techniques, task analysis, and other feedback methodologies”. User research has become entrenched in Epiphany’s CRO process, allowing us to understand what makes users tick and helping us uncover the ‘why’ behind analytics data. To allow us to run…
Usability.gov defines user research as “understanding user behaviour, needs, and motivations through observation techniques, task analysis, and other feedback methodologies”. User research has become entrenched in Epiphany’s CRO process, allowing us to understand what makes users tick and helping us uncover the ‘why’ behind analytics data.
To allow us to run qualitative and quantitative research for all of our CRO clients, we’ve recently completed the development of our in-house user research facility, Mindseye. Mindseye has been created with users at the heart. We’ve purposely created a relaxed, informal, and homely environment to encourage natural behaviour.
We’ve also invested in both desktop and mobile eye tracking technology which provides us with another layer of data to draw upon to support our recommendations. Eye tracking also helps us tap into users’ subconscious behaviour, meaning we don’t have to rely solely on what users tell us.
What Are Its Benefits?
There are many benefits to adopting the user-research method to inform a CRO programme:
Firstly, understanding why users are behaving a certain way can help us develop solutions that genuinely address this behaviour, increasing the likelihood that the solution will have the desired impact. For example, analytics data might tell us that the basket has a high exit rate. Without user research, we may hypothesize that the reason for this is the prominence of the promo code field resulting in us developing an AB test centred around reducing the prominence of the promo code field. However, as we’re not genuine users of that website and we don’t necessarily fit with the target audience or demographic, it’s likely our hypothesis could be wrong. Conducting user research means we understand what’s genuinely causing the high drop off, for example, lack of clarity regarding delivery options, and thus, develop a relevant solution which is more likely to generate a positive outcome and helps prevent wasting development resource. The impact of taking a more informed approach to CRO is illustrated in our 80% AB test win rate.
Secondly, user research helps remove opinion and subjectivity from optimisation efforts. Paul Rouke from PRWD describes the negative impact the traditional HIPPO mentality (highest paid person’s opinion) can have on optimisation efforts when he explains “the traditional HiPPO in business is the thing that so often is seen as the opposite of progress, engagement, leadership, inspiration, collaboration, and humility.” Running AB tests based on what the highest-paid person thinks, rather than tests informed by data can be the detriment of successfully implementing an optimisation culture within a business. Conducting regular user research highlights the importance of the user and helps communicate this to the wider business. Again, the ultimate benefit to this is that optimisation efforts can be focused on the things that genuinely impact users, increasing the chances of success.
Finally, user research can help with prioritisation. Prioritisation of AB tests is an extremely important aspect of the design process of any optimisation programme. At Epiphany, we’ve developed our own prioritisation methodology which includes scoring each test hypothesis on the amount of supporting quantitative and qualitative evidence. As such, conducting user research helps us validate test ideas by gathering an understanding as to the severity of the issue on user experience. This ensures we’re focused on what impacts users as opposed to what doesn’t adhere to UX best practice, which although has its place, tends to assume all users, websites, and businesses are the same.
How Does It Work?
There’s quite a lot involved in running user research and although it can be done quickly and cheaply, we’re firm believers that you get out what you put in. There are four key things to think about:
It’s important that the participants you include in your user research provide a true representation of your actual users. There are a few ways to recruit users from UX research platforms, via social media, or sourcing users from your existing database. Although demographic criteria such as age and gender are important, it’s also key to consider the types of habits and behaviours you want your participants to have, for example, what device/s do they use to access the internet, how does this differ depending on the task, are they the sole or joint decision-maker and finally, do they have a genuine interest or need for your product or service. Pretending to part with your money is very different from actually parting with your money, so it’s integral that the sessions are as realistic as possible to ensure that your participants are genuinely in the market for your product or service.
Top tip – Set up a recruitment survey for potential participants to complete. This should include answering questions on a range of different topics, for example, ‘Which of the following items are you currently in the market for? (select all that apply)’ rather than ‘Are you in the market for a new sofa?’. This prevents potential participants from knowing what the research is focused on and as such ensures they answer genuinely.
Writing a script for the research sessions helps ensure there is consistency across the sessions as well as communicate to clients or colleagues how the sessions will run. Script creation should be focused on the research objectives, e.g. the purpose of the research. Thinking about this should help you develop your key tasks, questions, and prompts. The script should also include rough timings to help you plan out the session and prevent over or under running. Including a time allowing task which will only be undertaken if the participant is particularly quick to complete the key tasks and provide feedback can ensure you make the most of all of the available time.
Top tip – Start the script with very open tasks that don’t lead users to behave in a particular way. If there are specific areas of the website you want feedback on, include more specific tasks later in the session. Always give participants the chance to interact and share feedback naturally first. If they don’t come across the page or element you were looking to gather feedback on naturally, this is an interesting insight in itself. We start the majority of user research sessions from a blank browser screen, allowing participants to browse for the relevant product or service completely naturally, before taking them to a specific website.
The key to moderating user research sessions effectively is to observe and listen. The less you speak, the better and the silence is often golden. Your role is firstly to reassure the participant about the session by explaining what will happen, being open, friendly and (where appropriate) informal and help participants relax into the situation. Being engaged and providing cues of encouragement when participants are giving feedback also reassures them that they are providing useful insight.
Top tip – Start the session with some pre-session questions. These questions should talk very broadly and then become more specific to the topic or service the research is focused on. For example, if you or your client sells holidays, then start out by asking about where they’ve been recently or any holidays they have coming up, then find out more about how they normally book, with what companies and why. These questions help you find out more about your participant which will help you run the rest of the session effectively, but this is also an opportunity to get your participant talking.
When it comes to analysis, it’s always best to observe live via streaming from the research facility. When observing the sessions, take note of what participants do as well as what they say (these things aren’t always aligned). Participants don’t always know when they do something interesting so keep an eye on the eye tracking to ensure you don’t miss insights that participants don’t necessarily verbalise.
Top tip – If there’s a large group of people observing the research, encourage everyone to note down their top 3-5 insights from each session on post-it notes. Reviewing everyone’s top insights between sessions will facilitate debate and help identify the key themes.
The Role Of Eye Tracking
User research can and is frequently conducted without the use of eye tracking. However, there are many benefits to investing in technology to help enhance the output of research techniques. Any effective CRO programme involves drawing on multiple data sources to validate AB test hypothesis. User research provides a strong source of qualitative evidence but including eye tracking overlays another quantitative source to further strengthen the supporting evidence.
Eye tracking also means the research sessions can be more natural, preventing users from thinking out loud as they go. ‘Think out loud’ is a common methodology, however, it can cause participants to alter their behaviour as they are forced to verbalise and as such rationalise their behaviour. Using eye tracking means you can use a different methodology called ‘retrospective think aloud’. RTA involved running post task retrospective user interviews where the eye tracking footage is played back to users which helps them recall what they were thinking and feeling at that point, meaning they can conduct the tasks in peace.
The final benefit of using eye tracking technology within CRO programmes is to validate insights gathered from heatmaps. Heatmaps play an important role in identifying optimisation opportunities. However, mouse and eye movements are often very different. It could be inferred that heavy mouse movement, for example over a section of content on a website, suggests that the user was engaged with that content but this isn’t necessarily the case. Being able to observe participants interacting with your website with eye tracking helps you understand if that mouse movement is accurate to where your users are engaging and interacting or not, helping you make more informed optimisation decisions.
What Results Can I Expect?
Here’s an example from one of our clients Goodmove. The user research that we undertook in Mindseye identified that participants felt that the homepage made them feel negatively, explaining that some of the terminology used felt ‘spammy’ and ‘click-bait’. This led to an ABn test run through VWO which experimented with the use of the word ‘FREE’ on the homepage. This test included multiple variations which included removing the capitalisation of ‘FREE’ and replacing ‘FREE’ with ‘personalised’. This test resulted in a 7% increase in sign-ups, highlighting the importance of gathering user feedback to support conversion rate optimisation.