Glossary Index

A   B   C   D   E   F   G   H   I   J   K   L   M   N   O   P   Q   R   S   T   U   V   W   X   Y   Z


Glossary Index

A   B   C   D   E   F   G   H   I   J   K   L   M   N   O   P   Q   R   S   T   U   V   W   X   Y   Z


Glossary Index

A   B   C   D   E   F   G   H   I   J   K   L   M   N   O   P   Q   R   S   T   U   V   W   X   Y   Z

...

Mobile analytics

...

Mobile analytics allows companies to track and analyse the behaviour of customers on mobile applications and devices.

... 

Mobile is an important part of any company strategy. Companies should take an omnichannel approach that includes mobile when creating a business strategy.

Mobile app customers tend to be higher value or more engaged.

Mobile app analytics can give companies a better view into why a customer converts or continues to interact with a company 


Q: What is mobile analytics?

A: Mobile analytics is the ability to measure how people interact with your mobile app. Tracking the behavioural analytics data for a mobile app in real-time will help a brand evaluate and adjust the product experience offered to customers. 

At the moment, most companies approach the problem from an omnichannel perspective. They want to drive engagement overall and they consider all of their digital channels together, rather than understanding different channels in isolation. This more holistic view helps optimise customer experiences across all touchpoints.

Q: How do mobile analytics tie in to the bigger picture for a company?

A: Mobile analytics is great to drive engagement and awareness, but at the end of the day, customers are going to convert over different touchpoints. If you measure the channel or mobile analytics in isolation, it's not going to tell you the real impact. Measuring lifetime value across all channels is a better measurement of user behaviour than measuring channels in isolation. 

If you combine the analytics from your mobile app with analytics from other channels, like web, email and social, you can see if mobile app usage had an effect on their ultimate conversion — even if the person didn’t convert or make a purchase, in the app. 

Some companies don’t differentiate between customers on the web and customers on mobile. How the company uses web analytics versus mobile analytics depends on the objective of what the brand wants to accomplish through the mobile app experience. For example, a company that offers a subscription membership will use a mobile app to connect with existing customers and provide experiences catered to them, whereas they would use their website for lead generation. In a case like this, success is defined differently on each channel because the goals of each channel are different.  

Mobile analytics can also help you to solve product experience issues. For example, based on analytics, a company can see that customers are closing an app because they get to a screen and don’t know what to do next. Because the company is tracking customer behaviour with mobile analytics, they are able to identify the problem and can then provide a solution. A company can also determine how the most profitable or engaged audiences use its app and then focus on improving those experiences — possibly with things like localisation and personalisation.

Q: What can mobile analytics tell you about specific customers?

A: Many people who are going to a brand’s mobile app are often the most engaged and most loyal — and they are higher value, meaning they generate more revenue. They generally tend to be further down the funnel — they are known customers rather than someone looking for a product or service. So, in that sense, your mobile app is a great platform to build a long-term relationship with your customers and help make sure they get great value from their relationship with you. 

You can use mobile analytics tools to discover usage patterns of customers that buy more and then develop experiences to encourage a specific behaviour.

Q: Why are mobile analytics important?

A: Since mobile customers are generally further down the funnel, measuring mobile analytics helps you to offer a better user experience within any digital property. It's going to help you to understand where customers are converting and where customers are falling off. If you optimise an experience with your most engaged audience, it's going to help you to create better engagement and drive more revenue to your company.

Some companies treat mobile as a side project because they think it competes with their website. When they have this attitude, they don’t pay as close attention to mobile analytics as they should. However, the recommended approach is to meet your customers where they expect to engage with you — whether that is on mobile or on your website — and give them the best experience you can. If you don’t prioritise mobile or at least give your mobile app the same treatment as the website, of course the customer experience isn't going to be as good and it will be hard for your customer to see the value and for your business to see the ROI from a mobile app.

If you don't measure your mobile app experience or every experience you're offering, you’ll be making decisions without knowing what's happening on the customer end. You really have to know how the customer is engaging and you can understand this with mobile app analytics.

Q: What are some of the metrics you would look at on a mobile app versus a website?

A: When a company adopts a true omnichannel strategy they tend not to have a specific metric on their mobile app vs their website, because success should be measured equally on any platform. A particular way of tracking success on mobile is through events, as opposed to pageviews for web. Events are actions taken by a customer, like a launch, a conversion or clicking a specific button. 

Through event tracking, product managers and analysts can have the flexibility to measure any behavioural analytics they need. 

However, there are many mobile app-specific events that help product managers understand their customers. For example you can measure daily active users (DAU) or monthly active users  (MAU), as well as the frequency with which a customer engages with the app. For instance, someone will be counted as a daily active user if they use the app today, but they are going to be a different kind of user if they use the app many times a day versus once a day. You can also look at how many customers have push notifications turned on and use that data to understand user behaviour.

The sessions are going to help you to know the level of engagement of one customer, because a customer could have many sessions today. So those are metrics that tell you engagement. You could also measure metrics related to revenue generation, like average revenue per customer or average transaction value. 

Retention analysis is a little different on mobile. You can see the number of customers who launched the app and then the percentage of customers that came back a month later. People interested in mobile apps are always looking at retention rates. While looking at retention, you can also measure how customers behave over time. Beyond how frequently a customer launches the app and how often they return, you can track how many people convert. 

Then in the mobile app there are also performance metrics — like how often the app crashes. For the most part, though, success metrics for the business should be global, not specific to the mobile app.

Q: How does cohort analysis relate to analytics?

A: Cohort analysis allows you to understand return behaviour over time or return behaviour among segments. You will know which customers are returning and why. You can create a segment based on returning customers after running a cohort analysis and then you could create another segment of customers that are not coming back and understand the underlying reasons they behave differently.

First, you need to define the group of customers you want to analyse. Then, you define a specific action you want the customer to take and finally a specific return behaviour. For example, let’s say you want to analyse android customers who launched their mobile app and placed an online order.

You can then use cohort analysis to compare varying levels of user engagement. You're going to be able to see which customers are returning and buying, so you can create a segment of those customers and then you can compare those customers to customers that didn't convert. You're going to understand what made the difference — what piece of data is having those customers behave differently.

For example, it could be that the users got an email or they were part of a test group that you were trying or they were part of a campaign or they got a coupon. When you start understanding the underlying reasons that they converted, you can apply what you learnt to audiences that are not as engaged.

Q: What job positions are needed to work with mobile analytics?

A: The product manager tends to own the product experience. Then, you can have a team made up of an analyst, who knows everything about reporting analytics and using an analytics solution and a developer, who adjusts all the measurements that they need to track. The product manager interacts with the analyst to make sure the reporting shows the correct definition of success.

Q: What are problems companies can run into with mobile analytics?

A: Companies can make the mistake of not tracking customers across devices and channels. It’s important to have an accurate view of the customer through their journey, because if they're engaging in one type of behaviour on one device and then different behaviour on another device, you may potentially offer them different experiences and it's not going to be cohesive. If you do track across devices, you’re going to be able to optimise the user experience and the cost of acquisition. Also, not tracking customers across devices gives an inaccurate customer count.

Brands should also implement A/B testing to make sure they are offering the best possible experiences to customers using the mobile app.

Q: How has mobile analytics evolved?

A: Mobile analytics has been around for about 10 years or so and at the beginning people were sceptical about whether or not mobile was going to be an important channel. Then around five or six years ago, people thought that mobile was going to be the first and only, but two or three years ago, they realised that it's really about that omnichannel experience, not the mobile-only experience or mobile-first experience.

In regions where Internet access is limited, smartphones tend to be the main point of access for customers. In these regions, a mobile-first strategy is very important. 

Eventually, with smartphones, everyone jumped on the opportunity to use mobile assets as a main channel. It wasn't until 2016 that mobile traffic passed desktop traffic.