October 2016

Predictive Analytics: Why it Matters

Predicting your customers’ activity would sound almost magical, but it is possible using predictive analytics.

Predictive Analytics: Why it Matters

In the recent years, big data has become a significant focus point for businesses. Gathering massive amounts of data is now routine for most of us. It’s not a question of whether or not it is important to collect information on your customers. The key points that should come to mind are the type of data to collect and analyse, and from there, the process to predict consumer behaviour based on patterns and trends derived from the data. Predictive analytics is on the rise, and now everyone can benefit from it.

What is predictive analytics?

Predictive analytics comes in when collected customer behaviour and information is analysed to predict how users react to certain marketing activities. From there, marketers can automate their processes with predictive planning. A business running with predictive analytics is based on algorithms, machine learning and analytics via identifying the future outcomes based on historical data.

As easy-to-use software and mass marketing tools have become available for smaller companies as well, predictive analytics has taken over spaces in marketing. With predictive analytics, businesses can determine future responses of their customers, their purchases, their activities on-site and promote cross-sell offers. It helps attract, retain and add value to the most profitable customers.

Predictive Analytics: Why it Matters

Where can you use it?

Predictive analytics can be beneficial for every company that deals with an online space. It can help you profile your customers better. Remember that the business which knows more about their customers has the bigger market advantage.

Predictive analytics shines when you need to profile your best, most active and most loyal customers. Profiling can reflect on what user journey they take, what products they prefer, how often they come to your business and so on.

Once you know enough about your overall and also the most loyal customers, you can quickly profile your prospects which can help you determine and acquire high quality leads to your sales. By focusing your sales team on high-quality leads, you will have a greater impact on your sales. Predictive analytics can create highly effective marketing campaigns as well and can help you to cross-sell to your loyal customers. And as a bonus, there is no better tool than predictive analytics to profile and value a loyal customer – it can get the most out from your loyalty program.

Primary examples of usage

The very basic predictive analytics technique is the A/B testing. For example, A/B testing on an email marketing campaign involves two email templates. An equal amount of traffic will be sent to these two versions, and the responses will be compared through this test. You can enhance this process with multivariate testing, where you compare not just two outcomes but multiple variables.

You can use predictive analytics technique with regression modelling. With the regression model, you compare multiple variables (predictor) to one variable (response). In an email marketing campaign, for example, you want to research how your users react to several variables, like send time, content, subject line and much more. All of these, you may want to research to a single variable, which is important for you, like click-through rate (CTR). In the end, your outcome should be a prediction, where you can predict how users react to different content, subject headline, etc., concerning CTR.

Predictive analytics is the brain of your marketing

Normally, you have tools and techniques you use, like direct mails, social media content, targeted ads and much more. You operate them separately most of the time, or you might use marketing automation which helps you save time and target more valued customers. Predictive analytics sits on the top of your marketing automation where you analyse all the data combined in your marketing and with systematic predictive models you can easily make your marketing automation more efficient.

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The Essential Guide to Mobile Analytics

How should we measure our mobile app’s performance? What metrics should we focus on? What are the basics of mobile analytics?

The Essential Guide to Mobile Analytics

Mobile is enormously important. Having a mobile app is a must for most businesses in today’s challenging business environment. But having one doesn’t equal to measure one. Tracking your customers’ behaviour in your mobile app is crucial if you want to evaluate your mobile app’s success. Tracking mobile user behaviour is almost the same as tracking users on a website, though some minor specialities are only available on mobile platforms. This article will give you a hand in understanding mobile analytics.

Getting started

The Essential Guide to Mobile Analytics

What is mobile analytics?

First start with what’s not mobile analytics. Our efforts have to be focused on mobile applications and not mobile optimised websites. The answer is simple: measuring a mobile optimised website is almost identical to measuring an average website. In this approach, we will only focus on mobile applications and the user interaction happening inside the app. Of course, every mobile application is different, but we can generalise it to give the basics of metrics here: a mobile app has content and multiple levels of screens or layers where users can interact with the said content. This is what we are aiming to track.

What are the essential tools for tracking?

We would hands-down recommend Google Analytics or Google Tag Manager. Google has some great features to track mobile apps; it can be easily integrated into the application, and it is free to use. If you prefer a more detailed version of tracking, check MixPanel, their offering is excellent, especially for mobile application analytics.

The primary goal for mobile app analytics

The Essential Guide to Mobile Analytics

Understand your users’ behaviour

Behavioural analytics is the most important focus point for any mobile application tracking. To understand your users’ behaviour, you have to implement events in your analytics system. Events are described as actions by users. Anything can be an action: clicking on a button, signing up, sharing content, scrolling through content, add an item to the cart, etc. Events can describe how users are engaging within your mobile app.

Define your goals

Before you focus on the understanding of your users’ behaviour, step back and define your business goals. What is your mobile app’s primary goal? Once you establish your goals, organise your events aligned with your goals. If your goal is to get users to consume more and more content through your mobile app, then content marketing metrics might be more important for you. If your goal is more on sales or signups, define your KPIs accordingly and set up events to support your success. You can set up events in any analytics tool you use.

Measuring retention

Where users drop off

It is easy to track which point users bounced off from your mobile app or spent prolonged idle time. By identifying these points (screens), you can quickly determine the stages in your app where users get a harder time. Optimise your mobile app’s user experience accordingly. For example, if your users open the register or signup screen but don’t register, that means your register screen is too complicated. Get on easy with it.

Where users need to be active and how to bring users back to the app

Some screens of your app have massive potential and users are using it, but they don’t take the effort to act accordingly? Try out new UX features; maybe it is not easy-to-use enough.

You can also track the app visits/opens. If your users have downloaded the app and used it as well, but they have been dropped off and opened the app a long time ago, you can pull them back in. Push notifications, emails and other tools can be useful to retain your users, and of course, you can measure all of them.

Location, sales and other basics

With your mobile app, you can track your users’ location. Of course, not everything and also some users won’t let you access their location data, but you will get a wider picture. If you spot any trends in location, let’s say you have higher than the usual amount of users from a particular location, you can add some features in favour of this place. You can also send targeted ads to that location which you can also track.

If your mobile app comes with an in-app sale or your mobile app in fact not free to download, you can track and monitor the sales volumes. These can are trackable with almost the same e-commerce funnels which you familiar with in website metrics.

All in all, you don’t need a professional analytics expert to track your mobile app. If you already have the resources to track your website or social media channels fully, you can easily switch the methods and practices to mobile app tracking.

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Virtual Reality: Reshaping Digital Analytics

The Future of Virtual Reality and Digital Analytics in measuring digital action and attention

Virtual Reality: Reshaping Digital Analytics

Although virtual reality is not exactly a platform for the masses, it is the most innovative approach to delivering content as of today. The platform, still in its early stages and loads of new creative ideas has already challenged the digital analytics. Virtual reality has the potential to redefine how we think about measuring the bread and butter of digital analytics: attention and action. This article helps you to understand the behind-the-scenes on this new trend.

The state of virtual reality in 2016

Virtual reality is A thing, but not THE thing

The advent of Facebook to the masses has been memorable for most. Back then, it was the thing. Virtual Reality is sort of like that. It’s a thing, everyone knows that, but still not the thing that shapes our lives. This platform is sure to bring about more innovations; almost all the major brands will come out with devices supporting this platform, albeit with a limited content format supported. Right now, you won’t read or follow the news on VR; you won’t buy your stuff with it, and it certainly will not be the platform where you hang out with your friends online. At least, not yet.

Virtual reality platform is not that new

An internet forum is not much different compared to an IRL room full of people – maybe net forums have more trolls – and web marketing in the core definitions is not much different from selling stuff IRL; virtual reality isn’t that much different either. The platform currently has some technical and user experience limitations, but the base remains the same. Analysing data collected through virtual reality is also highly similar to the thing we now know – digital analytics. We measure the same things on virtual reality that we measure in digital analytics: attention on digital content and interaction between the content and the user.

The question would be, how it compares to traditional analytics.

Virtual Reality: Reshaping Digital Analytics

The difference between traditional analytics and virtual reality analytics

Digital analytics is all about measuring how users interact with content in a pre-set environment which can be anything from a website to an app or a feature. Virtual analytics is not much different at its very core.

Measuring actions

Measuring actions is pretty much the same in virtual reality as it is in traditional digital analytics. In traditional marketing, when a user clicks on the Purchase button, we can measure the interaction. We can do the same in virtual reality when we measure how many users click-looked on a virtual button. This process can be repeated and used as a default setting for all action-based virtual reality environments. Therefore, measuring an action in virtual reality is easy, and it is pretty much the same as the traditional metrics, only with some slight differences in definitions which can come from the differences between the two environments.

Measuring attention

Attention is one of or maybe the most important insight we need to figure out with metrics. It doesn’t matter how many users visited your site if no one paid attention to your content and bounced off. With today’s digital analytics, we do have some metrics which can define the depth of users’ attention. Mouse overs measure where the users’ cursor are, scroll depth also gives us a clue on how the content consumed and we do have some other sophisticated metrics like frequency, content recycling and page depth.

In virtual reality, measuring attention is a bit different because of two reasons. First of all, due to the platform’s environment, we know exactly where the user’s looking. Second, due to the evolving digital content in the virtual reality environment, we have more data, which makes things a bit more challenging to measure. The immersed amount of data processing is more likely a technical challenge and not an analytics one so for now, let’s focus only on the first: the user’s exact attention.

In virtual reality we know where the user’s eyes are. With virtual reality, we can also measure the very essential difference between looking and watching something. The depth of a website, for example, can be measured with frequency, page depth and other metrics. This can be achieved in virtual reality by measuring the amount of time spent by the user looking at the same direction on the environment. With heat maps and other analytics tools, we can accurately show how users are paying attention to our content. This can be very challenging to traditional display advertisers…

A very different user journey

Drawing up a funnel or a user journey is pretty straightforward with traditional analytics. In virtual reality, the user journey is way more complicated because of one simple reason: users love to wander around virtual environment just for fun or the sake of it. This creates an extensive amount of digital noise and leads to lots of miscalculations. Establishing virtual reality zones, where the attention is highly relevant might be an option, but the technology is just not sufficient enough yet to accomplish this challenge. Also, user journey measurement needs users, and as of now, virtual reality is still in its early adopter stage. Once masses are using this platform, the answer for measuring virtual reality user journeys will surface.

How virtual reality will help us shape traditional analytics

We have learned that measuring actions is pretty straight-forward. Therefore we should not overcomplicate it in digital analytics either. Measuring attention, however, is another story. Virtual reality might help us to learn the real value of our display ads and the actual value of user experience design. By adding sufficient and precise metrics to measure the users’ attention, we can make better user experiences and direct more productive ads to users. We have exciting times ahead of us!

Interested to know more about virtual reality? You’re more than welcome to book a Q&A session where we answer all of your further questions.

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Custom Analytics Report: 10 Tips for Presenting the Right Data

Finding insights from a dataset is one thing, presenting it to drive change in your business is another. As packaging is necessary, presenting actionable insights from data is also crucial. Here’s how to do it flawlessly.

Category: Content Marketing Title: Using Videos Effectively: 3 Practical Tips

We have seen so many bad examples and sat through hour-long presentations on KPIs and measurement charts with a great set of data pieces and an endless number of slides. So we decided to show you the best way to present and report your data. This process is applicable to all kind of businesses and business cases with any analytics background and data coming from any measurement system. This simple process will help you get the best out from data.

Finding the best structure for your presentation

The first phase of the process is data findings. From any analytics system, you will have huge chunks of data streams that will generate results for you. You spot a correlation between two variables or you spot a trend in a timeframe on one metrics. If you have goals already, they can help you focus on where to look for findings.

Present your findings clear and straightforward. This is what you have found, and you think this pattern means something to your business partners. A simple visual representation can help evaluate the findings.

Custom Analytics Report: 10 Tips for Presenting the Right Data

Highlight what you learn

The second phase of the process is learnings, takeaways or insights. This is where your analytics experts come along, analyse the findings and come up with great learnings. Learnings are explanations of data results: a simple objective explanation of the ‘Why is this happening?’

Learnings have in-depth meanings. Your business and business case gives the context for every learning and drives them to actionable insights. Present your learnings in correlation to your business: what benefits will our business have with these learnings?

Set the process of actions based on learnings

A perfect data presentation is nothing without actionable insights turned into true marketing actions. You’ve got your findings, you’ve put them in context with the learnings, now what will you do to drive the business forward? In this part, a strategic mindset works best, but lots of marketing expertise also won’t hurt.

Going technical: the length and the illustrations

Custom Analytics Report: 10 Tips for Presenting the Right Data

Short is Effective 

Keep the length of your presentation simple and quick. Remember, the insights presentations primary goal is to support decisions, so don’t get too complicated. If your presentation is too long, your audience will miss your points. Present as much data on your findings as it’s necessary and supply further data sheets as appendixes or extended content read after the presentation.

Your learnings have to be super short, one statement only. Learnings are summaries of data. Sum up your learnings so they can be easy-to-understand and digest. Don’t fall for definitions and data-speak, use plain English.

Actions should be visual and should highlight a process. Actions lead to a journey; the series of processes to change your business. Actions should be informative and have to detail responsibilities, deliverables and resources.

Illustrations, Infographics and Flowcharts

Presenting data is rarely consumable in raw data sheets. Graphs and tables have to be descriptive and have to highlight certain findings only. Using a graph just for the sake of it can distort the outcomes.

Infographics sometimes are as useful as charts and tables. But don’t make them as your go-to illustration for your presentations, they shouldn’t overflow the whole.

Flowcharts can help you to highlight learnings and actionable insights. Simplify the data presentation process and only focus on just a few learnings and use a diagram to illustrate the super-focused pitch of your learnings.

Remove any clutter from your data sets and only focus on the important issues that tell a story.

Custom Analytics Report: 10 Tips for Presenting the Right Data

Ten tips you need to know before going on a data presentation

 

  1. Always remember to put your data into context when reporting. You can’t assume everyone knows what you do and how the data metrics work.

 

  1. Supply an appendix after the presentation that highlights your points and serve as evidence

 

  1. Don’t go crazy on charts, tables, flowcharts or infographics, find the best balance and the best tool to deliver your message

 

  1. Remove any clutter and have a pinpoint focus with your data, only present what’s relevant to your audience

 

  1. Findings are charts or tables, learnings are flowcharts, actions are processes

 

  1. Keep it simple in language and very short on length

 

  1. Usually, people are afraid of numbers – make it easier to digest with colours, animations and pictures or metaphors

 

  1. Use timescales or periods to showcase growth and trends

 

  1. It rarely works out if you use real-time data, use already analysed datasets

 

  1. Always be open for questions

 

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