User Acquisition

A Guide to e-Commerce Tracking

Are you getting the most out of your eCommerce website? How is it doing regarding performance? If you don’t know the answer to this, most likely, you haven’t measured its performance. This has been discussed in general during the previous article, but we will focus on e-Commerce websites.

A Guide to e-Commerce Tracking

As earlier discussed, your site’s Key Performance Indicators or KPIs has a significant reliance on your website goals. In this case, an eCommerce website. Below are some important KPIs to measure:

  • eCommerce Conversion Rate
  • Cart Abandonment Rate
  • Average Order Value
  • Products Per Order

If you have an existing eCommerce website, cart abandonment rate should be one of the first metrics to focus on and improve. It’s important to analyse what made these visitors decide not to go through with the checkout process.

Here are some examples of tracking tools best suited for eCommerce websites:

The tools stated above aren’t the only possible tools best suited for eCommerce tracking, but for this article, we will highlight MixPanel as the tool of choice.

Why MixPanel?

MixPanel’s user interface and design are user-friendly and very easy to use without the need for much tuning and settings. MixPanel also lets you view data, updated in real-time, which has advanced features you can use to filter for certain events.

Pricing for MixPanel depends on the number of data points you need. Data points are counted everytime an event is tracked with MixPanel. They are described as tracking credits. A free account is credited with 25,000 data points, to begin with. Paid plans start from $150/month for 500,000 data points, $350/month for 2,000,000 data points, $600/month for 4,000,000 data points, $1000/month for 8,000,000 data points and $2000/month for 20,000,000 data points.

Feel free to connect with us if you wish to learn more about your eCommerce website performance, opportunities to improve its performance and what tools are a perfect fit for your business.

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A Guide to Social Media Monitoring

Social Media has always been a valuable tool for reaching out and building relationships with our market. Not only that, but it has also proven itself to be a significant element in boosting sales and increasing ROI.

A Guide to Social Media Monitoring

To make sure that our social media marketing efforts produce the best results for us, it is important that we keep track of the right social media metrics or KPI beyond just likes, follows and shares. Below are some of the metrics we monitor:

  • Shares
  • Likes
  • Follows
  • Profile Visits
  • Clicks
  • Mentions
  • Engagement
  • Impressions

There are various social media monitoring tools each with their distinct features that let you track one or more social media KPI from one or more platform.

Here are some examples of social media tracking tools:

Note that are various free and paid tools that enable you to monitor your social media other than those referred to above. In this article, we’ll focus on mentions as a social media KPI and highlight Mention as the tool of choice.

Why Mention?

In a nutshell, Mention can act as a sieve by filtering mentions and signals to make sure you receive only those relevant to you and filter out the noise. Need a report? No problem! You can export stats into CSV or PDF files to share with your team!

Need on-the-go monitoring? Mention has a mobile app which you can use to engage with your audience where you are.

Mention can be integrated across various social media platforms such as Twitter, Facebook, YouTube, Instagram and Pinterest, among others. With this set-up, you can engage and interact from mentions by just using your Mention dashboard. No need to switch in between tabs to access your various accounts.

Prices for Mention start from $29/month for individual users to $99/month for small businesses, and you can sign up for a free trial to get a feel of the product before subscribing for a full plan.

Feel free to connect with us if you wish to know more about Social Media Monitoring and the tools that fit your business.

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A Guide to Web Traffic Tracking

How’s your website doing? If you have no idea, then you probably haven’t been measuring your web performance. If you don’t measure your web performance then you can’t possibly put a value on your website nor can you determine how to improve your site’s performance.

A Guide to Web Traffic Tracking

Your Website Key Performance Indicators or KPIs pertains to a set of measurement of performance regarding your website goals. To define your KPI, you have to set specific goals depending on the type of your website.

Most common types of KPIs include, but are not limited to:

  • Website Traffic
  • Conversion Rate
  • Bounce Rate
  • Lead Generation

Let’s focus on website traffic for now.

Website traffic, in general terms, refers to the number of people visiting your website. This could be further broken down into paid traffic, organic traffic, direct traffic, and referral traffic.

Here are some examples of website traffic tracking tools:

Various free and paid tools enables you to track your website traffic aside from those mentioned above. Today we will highlight KISSMetrics as the tool of choice.

Why KISSMetrics?

KISSMetrics hosts a robust set of features that lets you track beyond just page views. Not only that, but the setup process is considerably easier than most tools as it does not require you to write code. KISSMetrics makes a great alternative to Google Analytics.

With KISSMetrics, you can track visitors and monitor their activities even before converting enabling users to gain insights of consumer behaviour before purchasing. With this knowledge, you can improve your funnel according to consumer behaviour to increase conversions.

KISSMetrics also hosts unlimited reports for funnels, cohorts, retentions, customer profiles, conversions and even A/B Test Reports with real-time data.

As powerful a tool KISSMetrics may be, it does not come cheap. The Startup package starts at $120/month, the Growth package at $400/month and the Power package at $600/month. But, worry not! You can get a feel of this tool by requesting a demo account before deciding to shell out cash for the full version.

Feel free to connect with us if you wish to learn more about website performance metrics and how to leverage them to improve your website performance.

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How to Run an A/B Test with Google Analytics

We’ve covered the basic introduction to A/B Testing and why it’s important to your business as well as mistakes commonly made in A/B testing in the first two articles. Today, we’ll run you through the process of setting up an A/B Testing experiment.

There are various A/B Testing tools available if you search around, most of which are paid software. But since not everyone is ready to shell out money just to test them out, we’ll highlight one of the known free tools around which is Google Analytics Content Experiments.

How to Run an A/B Test with Google Analytics

What are Content Experiments?

To explain better, here’s how Google Analytics describes the Content Experiments process.

Content Experiments uses a somewhat different approach than standard A/B and multivariate testing. Content Experiments uses an A/B/N model. You’re not testing just two versions of a page as in A/B testing, and you’re not testing various combinations of components on a single page as in multivariate testing. Instead, you are testing up to 10 full versions of a single page, each delivered to users from a separate URL.

While it’s mentioned that you can test up to 10 full variations, we would still recommend to keep it around 2-3 only.

Before we lay out the steps to setting up an A/B Test experiment, be sure that you have you have properly set up your Google Analytics account and that you have your goals defined. Need help? Feel free to connect with us!

 

What to Prepare Before Setting Up the Experiment

  • Test Objective
  • Original page
  • Variation
  • Analytics tracking code properly added to the pages

How to Create a New Experiment

google-analytics-experiments

 

  1. Sign into your Google Analytics account
  2. Choose the website you wish to test
  3. Go to Reporting tab
  4. Go to Behavior
  5. Click Experiments

You will be asked to set following information:

google-analytics-content-experiments

 

  1. Name for this experiment
  2. Objective for this experiment
  3. Percentage of traffic to experiment
  4. Distribute traffic evenly across all variants
  5. Set a minimum time for the experiment to run
  6. Set the confidence threshold

After defining the objectives, you will be redirected here:

google-analytics-configure-experiment

You will be asked to add the URL of the original page and the variations. Page preview will be shown as thumbnails to help you make sure you set the right URLs.

After clicking ‘Next’, you will be prompted to set up your experimental code as shown below.

google-analytics-experimental code

This section will let you manually implement the code to run the test, or you will be given an option to email the webmaster to run the test.

google-analytics-review

After clicking ‘Next’, you will be able to review and validate the experiment code. Just click on ‘Start Experiment’ and you’re A/B Test will begin to run.

Feel free to Connect with us if you wish to know how we can help you with your A/B Testing experiments!

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Big Data, Small Tweaks, Big Results.

Our clients occasionally ask us what big data is and how we can use it. We asked our data guy Shaun to explain this for us.

What is big data?

Big data usually means a large data sample that needs advanced statistical analysis techniques, and computing power to process and provide deep analytical insights. The bigger the data, the more computing power needed.

We use big data techniques to analyse customer interactions, everything from phone calls, POS transactions, website visits, and potentially data mining public profiles to build profiles of customers that can be used to optimise marketing, and target only customers who will respond positively.

But it is a fluid term because the size of data sets vary, and with that, the tools that we use vary, and what they tell us varies. The easiest categorisation is to look at Small DataBig Data, and Huge Data.

Small data

We all work with small data all the time, but generally it is data sets that can be managed and manipulated in Microsoft Excel. Excel has a limit of 1,000,000 (1M) rows, and has limited equations and functions that it can run. The work that we do with Google Analytics would usually be small data, but could be big.

We would use small data when we are looking at transactions and orders for an online store. For a store with hundreds of transactions a day, a few tens of thousands of transactions a year with a few thousand customers. With some simple manipulations, we would be able to show graphs explaining insights such as average order values, demographic information, return customer volume and other details. This is usually sufficient to explain to decision makers what is going on with their business.

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Big data.

Analysis of big data requires you to use complex tools to leverage the full computing power of a PC (or mac) to complete analyses, and predict customer behaviour. The biggest opportunity we see initially is to help our clients identify common behaviour among users prior to taking a certain action, and then prompting the user to take a certain action.

As an example, we may be able to identify loyal customers that are most likely to leave in the next couple of months. This would allow you to take action (call the customer or email) to re-engage them. For example, the data may show that 30-35 year old Males are unlikely to shop if they don’t receive an email every two weeks. With this information we would intervene to keep them as customers. This would still probably be small data, we want to go further; if we can analyse how each customer uses a site, gain access to customer service phone data, individual data on whether someone opened an email, we can collate this and predict all sorts of future behaviour. We could run data, and predict that a particular group of people would respond best to a promo, and just target it at them. This is often called predictive analytics.

Running this data requires the full processing power of a computer, and can take from minutes to hours to process.

Huge Data

It’s the same as big data, but the processing power needed is more than a home computer or desktop can handle in a reasonable period of time. In these cases, we can hire processing power in large servers to process the data for us.

Let us know if you have any big data projects in the works, want to make the most of the small data you have available.