In the second part of the contextual series, I will be continuing from where I left off last time with User Onboarding. I’ll be covering how to make your application relevant after the user has gotten past their initial experience. The goal is to engage with the user in a way that will retain them and have them spend money.
The first few minutes of the user trying your product is crucial. In that short period of time they will decide whether they will uninstall it and move on or keep trying it out. But the next 30 minutes are also just as important. Like a drug, you want the user hooked to your product, you want them to feel as though they are dependant of it. The most common way products get you addicted is through social engineering by getting you to engage with people you know. While it is something I would recommend each product would do, the product should be able to stand on it’s own even if the user has no friends.
Let’s assume that your product is capable of tracking a lot of data, including basic user profiles to narrow down their demographics and activity history. The data can be used to improve your analytics, determine business logic, and can be used to feed into your contextual engines.
Your demographic data is useful for narrowing down possible trends within your product and understanding who to target in future advertising campaigns. Understanding the age groups, the countries and perhaps even gender of people who love your app will allow you to focus your money on those groups. Advertising is far more powerful today than it was 10 years ago and it will only continue to improve. You are able to specify the markets you want to go after that way your user acquisition costs are reduced drastically. Acquisition costs improve because the churn from an ad click can be severely improved. Perhaps when you did not target, 15% of ad clicks became users. By targeting your ads, 60% of ad clicks become users.
The demographic data can tell you what you may need to add to your product next. Your analytics may tell you that most of your users are from the UK. If your product allows regional content or content that would be relevant to UK users, this is when you may want to focus on those users to some extent. However, with many apps regional focus isn’t possible through content, but can be done through ads that highlight events that are ongoing. Many people were affected in the floods just a few weeks ago in the UK. Making a push to show your support for those people, providing contributions through user activity in the app would go a long way to having your users love you.
Not all changes are updates should be done manually when using your demographic data. The data should be used to identify trends between different groups in your product. Recommendation engines are extremely powerful, but one of the easiest setups you can do with software like Hadoop is to feed demographic data and allow it to make recommendations for the user group that comes on. In most cases products have content of sorts that users will want to cycle through. Whether you have an online store, are a picture viewer like 500px, or have a streaming video service, users will appreciate seeing something more catered to their needs.
The activity history is crucial and often overlooked. It allows you to understand various user funnels, paint a more detailed picture on your top spenders, and pinpoint potential problem areas in your product. Whenever possible, intersect the activity data against the demographic data. That way you can begin to find deeper answers to your questions like “which ad generates the most amount of money for me?” or “how long do users from my top 5 countries use my product in a week?”.
The activity history can also be used to make recommendations. For instance, a user going into an online store may search for technology and start looking at technology related products. Next time they come back to the homepage where you display top products, you should display top technology related products. Users know what they want and in order to push them towards using your product and spending money is by pushing them towards things you think are relevant to them. The same can be said with news content or videos in your music streaming service.
In some cases, activity history can be useful for making predictions. Companies like Mashable are using predictions based on real time analytics to determine whether they will promote content called Mashable Lift. Essentially what Mashable is doing is taking content that is becoming popular and marketing it and pushing it out to get more viewers. Whether it be through social media or advertising, they are able to leverage something they see becoming popular and riding that wave. App stores like Apple and Google also showcase new and rising apps to push more users to installing them. This works wonders for the companies hosting the content since you have popularity, word of mouth and various forms of advertising and PR that talk about the content.
Improving User Experience Improves Engagement
In order to have your user more engaged with your product, you must be more relevant to the user. Whether it be focusing some content or promotions around your most popular user groups. Showcasing content that the user who just signed up would likely enjoy. Pushing the user towards content that is popular and gathering a lot of attention. Or having the user’s content continuously adapt based on their activity. While they may seem like a lot of extra work on the backend, they drive the user experience in a meaningful way and will have users far more likely to be engaged.
I’ve used this sort of data to push reengagement of users in various different ways as well. By sending emails or notifications to users that are very relevant to them, you can likely bring them back to your product if you haven’t seen them in a while. If you are an online store, know the user is male and likes to buy gadgets, why not send the user an email showing them the latest gadgets that may appeal to him to get him back to your app.
Bottom line is that all information you have on the user is information you can use to make their experience better and make them want to use your product. I haven’t provided very many details on how to implement many of these techniques yet. My goal through further of these posts would be to begin diving into setting up methods for tracking data, feeding it into analytics, using recommendation engines and improving your user experience. The contextual aspect of these posts are to provide an experience that may be different between users. We are all individuals with different needs and technology should be reflective of this.
As usual, feel free to post a comment or contact me through any social network if there is anything you’d like me to focus on in this series.