Almost nothing happens in today’s technology-laced world without the influence of analytics. Whether it be the decision of which variant of coffee is preferred by customers in a certain locale or the strategy of choice to get a particular political leader elected, your behaviour, analysed through the numerous data points you generate online and offline, determines most decisions that major companies and organisations take today. While web-based analytics has been one of the earliest forms of digital analytics, a relatively newer form of analytics is app analytics. There are quite a few companies in India that are harnessing the latest in technology to bring it to newer places. But before we find out more about them, we need to know what constitutes app analytics.
Web or App?
We’ve experience web analytics and advertising based off of it for a long enough time to know that it works based on your actions online, such as when you’re going through an e-commerce site, searching on a search engine, and more. While it wouldn’t be inaccurate to say that app analytics takes the same idea and puts it on native iOS and Android applications, that wouldn’t nearly suffice as an explanation.
There are quite a few metrics that are technically the same across both avenues – active users, conversions and more. But beyond this, there’s a whole lot of stuff that puts app analytics ahead of web analytics in its ability to predict and understand user behaviour. “Basic business analytics will help businesses track their sales and marketing spends, growth, revenues, etc. that have an indirect bearing on the end customers”, says Anand Jain, Co-Founder, CleverTap, a mobile marketing startup, “With app analytics, the end users can be identified, segmented, targeted, and monetized almost instantaneously. In a way, app analytics allow app businesses to mobilize every single app user with pinpoint precision and accuracy.”
Here’s a more specific example – most websites still treat people as ‘visitors’ if the website is open and continue to track them as active users regardless of whether they’re actually on the website. The same is not an issue with apps, as most app analytics platforms stop tracking a user after 15 seconds of inactivity. Don’t even get us started on the ability of apps to send targeted push notifications and alerts to users – websites are only catching up to that now.
Analytics = tracking, yet, merely tracking MORE data doesn’t always lead to better analytics. Choosing the right metrics is as important. There couldn’t be a more relevant time, even though apps have been around for a while. There are more apps than ever before now, and multiple app analytics startups have been acquired or funded in 2017. There are a few important reasons behind that:
- Analytics for product improvement: App analytics has gone much beyond marketing, and at this point, analysing user uninstalls or drop-offs shows developers where they need to make improvements.
- Opportunity cost: Today, users are literally flooded with promotional messages and push notifications, so it is important to make them contextual and personalised – and that requires knowing their behaviour.
- Return on investment: Comparing the cost of user acquisition via different channels, of which there are too many today, with the marketing investment shows companies where to put their money.
There are different ways in which companies are putting both new and tested metrics to use. MoEngage, a user engagement and analytics platform, outlines many such app analytics metrics as a part of its offerings.
“Chillr – (a) multi-bank payment and transactions app,” explains Nalin Goel, Product Head at MoEngage, “combines business intelligence data like customer micro-moments, customer transaction insights, and such with app analytics to drive customer engagement. Key Metrics such as Usage, DAUs/MAUs, Conversions, Sessions & Funnels have become basic in mobile app analytics.”
State of the art
Beyond the metrics, there’s technology at play that makes app analytics what it is today. Companies like MoEngage and Clevertap have employed machine learning and AI to predict user behaviour successfully based on the data available to them. Data is not just analysed to create insights based on ‘what doesn’t work’ but also to identify possible solutions based on ‘what works’.
Even in the choice of metrics, companies that offer analytics services are gradually moving on to more relevant metrics. Beyond things like downloads, opens and crashes, the focus has moved to retention and repeated conversion to increase something known as the Lifetime Value (LTV) of a customer – which is essentially a prediction of the net profit attributed to the entire future relationship with a customer. Tools like Dynamic Product Messaging from MoEngage that implements AI and Machine learning to send highly customised push notifications is an example of this focus in action.
This new direction has led to things like qualitative analytics taking importance over from sheer numbers – answering questions like ‘why does the app crash?’, ‘why does a user leave from a particular screen?’ has brought in tools like touch heatmaps and user session recording analysis. In addition to this, a strong focus right now is to reduce the number of SDKs that are required for app analytics to make the life of developers easier.
There is a lot of talk about bots replacing apps, which is very similar to the popular sentiment that emerged back when apps launched about apps replacing websites. The results will most likely be pretty similar as well. Apps aren’t going anywhere anytime soon. However, that does not mean that we can discount the usefulness, and hence, the significance of chatbots when it comes to analytics. They are seeing increasing use as a point of contact between a user and a company, and not taking chatbots into account in any marketing and analytics offerings going forward would be shortsighted.
“Chatbots will work with apps and websites in the future to enable better customer service.”, says Nalin Goel, “Similarly, we can witness an increasing trend, where analytics from chatbots will be integrated with analytics data from the app and website. Analytical data from chatbots can be used to predict customer behaviour, their level of satisfaction with the product/ service and even serve customers better.”
On the other hand, tech like augmented reality and smart assistant devices are pointing toward a different future – one without apps as we know them. In that future, app analytics will have to evolve into a new paradigm or perish as an archaic tool that will be of no use to anyone but historians.