In marketing terms, personalization is the ability of a business to offer products, services or content based on each customer’s individual needs.
Let’s use a real-life example. Imagine you like pineapple pizza.
Back in 2018, SheerID and Kelton Global conducted a survey which showed that over two-thirds of internet users in the US took personalized offers more seriously than mass mailings, with 94% of respondents admitting they wouldn’t want to miss out on a unique gated offer.
Over the past four years, personalized marketing has only gained traction among customers. This also includes mobile marketing, as people seem to have grown tired of generic experience, irrelevant content and template notifications. Users want apps to be geared toward their individual needs.
The idea of mobile personalization is to abandon the broad uniform approach to the entire audience and start offering custom experience to each individual user.
In mobile, personalization comes in the form of push notifications, recommendations, discounted offers, etc. and is often based on the following data:
Today, the main personalized marketing technology is predictive analytics based on machine learning. Both of these areas rapidly evolve, become more and more effective, and interlace with customer relationship management (CRM) solutions, so we can expect the scale and quality of personalization, including in the mobile segment, to reach new heights in the coming years.
There are many reasons why all mobile app owners should consider personalization. Compared to the standard ones, personalized offers are essentially a shortcut to your goals, enabling you to increase audience loyalty, LTV, retention and conversion rates, etc. at a quicker pace.
This has been proven, among other things, by our recent MyTracker experiment. We spent three months testing MyTracker’s Personalize model in Hustle Castle and managed to achieve a 23% increase in average revenue per user (ARPU) with personalized offers for gamers.
Let’s take a more in-depth look at how mobile personalization affects some of the key metrics.
Personalized offers can be a reason for your users not to ignore or abandon your product. The better you cater to a person’s specific needs, the more loyal they will be to your brand.
Attractive offers, useful recommendations and discounts tailored to the user’s individual needs encourage them to regularly come back to the app. For example, a study by Wirecard found out that people are 75% more likely to make repeat purchases if the company has a loyalty program.
Moreover, 33% of customers abandon a business relationship because personalization is poor or non-existent. At the same time, only 28% of all mobile apps were personalized as of 2019, according to Evergage.
These figures prove that personal offers can give you a competitive edge in an oversaturated market, reduce the churn of existing audiences and consequently increase the retention rate.
If you want your users to remain loyal, consider making their experience more personalized starting today.
Surveys show that personalized marketing is currently the No. 1 expectation among mobile app users.
Offer personalization completely transforms app user experience. After receiving a personal offer, 41% of respondents start searching for products they could apply it to. Furthermore, 72% of users only engage with personalized messaging.
Besides, people like the feeling of being cared for, even if by a piece of software. Personalized communication gives the user the illusion of a real person on the other side of the screen, which stimulates engagement.
Mobile app personalization can significantly increase your conversion rate through individual discounts, one-click purchases of favorite products, offers based on search queries, and many other features.
Users who receive a personalized offer of any type are much more likely to end up purchasing the product or service. It’s no wonder that 93% of companies reported an increase in revenue after introducing advanced personalization.
Personalized marketing does not always imply tailoring offers to each user’s needs – there are also more generic solutions perfectly suitable for the mobile segment.
Let’s look at the five basic levels of offer personalization, starting with the broadest one.
This is the broadest type of special offers, which covers all or most of the audience and usually applies to the entire range of products or services.
This is personalization at “level zero”. You don’t need any special tools to set it up – for example, you can simply offer a 20% discount on all burgers in your food delivery app:
Loyalty offers usually only apply to recurring customers and are narrowed down to such things as a specific brand in e-commerce or an in-game item category.
At this level of personalization, you can offer loyalty program members a choice: whether to spend the points they've collected on a discounted item or double them for the next purchase.
Targeted offers are generated by a business owner for a certain target segment of the audience. User segmentation can be based on a host of factors that range from demographics to purchase history.
Here's an example by SHEIN app of how you can offer first-time customer discounts:
Personalized offers also target certain audience segments but rely on a narrower segmentation compared to targeted ones. This is the most in-demand level of personalization in today’s mobile marketing.
The idea is to segment users into small groups based on their gender, age, location, device model, and other criteria that may be relevant to you, and then use the most appropriate offers for each individual segment.
For example, you can offer top picks or a specific playlist to users who like a particular artist.
This is the highest level of personalization. In an ideal world, these are offers that are unique for each customer and depend on their profile and previous actions.
Good examples would be a playlist generated automatically based on the user’s music preferences, or an individual selection of background music or sounds for working and resting.
To achieve the highest degree of personalization at the level of individual customers, businesses rely on special techniques, primarily machine learning and using CRM data.
The independent studies listed above clearly indicate one thing: users want more personalization from mobile apps.
But what exactly can you offer them? Let’s look at a few popular types of offers out there.
Since we’ve covered what exactly you can personalize, let’s talk about how you can do that and look at the four most popular approaches with examples of how they’re used in the mobile segment.
Launching personalization by segmenting the audience based on its data, demographics and behavior is a tricky but very effective strategy.
It's not enough to address the user by their name – a data-driven approach requires the app owner to gather lots of data and, in particular, analyze all user events, such as content views, response to notifications, and purchases. The more information you manage to get, the more effectively you can personalize your offers.
Generate offers based on user behavior patterns.
Different user groups want to interact with you in different ways; for example, only via the app or through push notifications, emails, or messengers.
The preferred channel says a lot about the information the person is used to and wants to consume. Use this to your own advantage and create unique content for each of your channels.
For instance, customers might use a chat bot to find information about your product, mailings to access relevant content, and push notifications to receive hot offers.
Determining user locations is not just about localization by region, language, and prices adjusted to the audience’s purchasing power. Knowing the locations and preferences of users enables you to make unique offers with high conversion rates.
According to Localytics, 34% of users find special, location-based offers to be the most useful. Furthermore, nine out of ten marketers confirm that location-based marketing increases sales.
This strategy is not applicable to all types of apps, but if it’s relevant to you, try sending the user a promo code for a discount at a nearby store, a restaurant recommendation, or an alert of approaching rain.
If an individual approach to customers makes such a big difference, why are the majority of business owners (both inside and outside the mobile segment) still in the early stage of implementing personalization or have yet to even begin pilot initiatives?
The main reason is that companies often face several major challenges when attempting to make a shift towards personalization.
According to a McKinsey & Company study on personalizing user experience in retail, more than two-thirds of business owners find the need to collect, store and process data to be the greatest personalization challenge.
First of all, businesses lack the resources to implement, configure, and maintain the necessary technology, with 67% of respondents admitting they don’t have the right personalization tools.
Secondly, they often do not have enough expertise. About half of respondents say they lack experience in analytics and big data, and 41% state they can’t find a reliable partner to delegate these tasks to.
Mobile personalization implies that every customer interaction with the app contributes to improving user experience. You collect data with an aim to offer more relevant content, products and services tailored to the customer’s needs.
But what if that data is incomplete, unreliable, obsolete, or worse, completely false? Flaws in your data collection and processing system can undermine your ability to offer unique experience to your customers.
Imagine that a user who doesn’t have pets mistakenly visits a website with pet products. If the engine blindly relies on past interactions, this person will be receiving irrelevant offers to buy a pet bed or bowl for a while, which can be annoying.
As you personalize the app, you get a better understanding of your customers and things they enjoy and consider important. However, in adapting to their needs, you need to avoid both over- and underpersonalization.
Sometimes it’s not cost-effective or even feasible to create personal offers for each individual user, so you may want to opt for segment-based personalization instead. The problem with that is that lower levels of personalization (aimed at a broader audience) imply a lower chance of hitting the target.
Imagine that you have noticed a certain pattern: for example, you have realized that user event A (e.g., logging in) is most often followed by event B (e.g., repeating the previous order). This pattern is applicable to a third of your users, with other options being much less popular.
The personalization engine may quite reasonably start displaying offers to repeat the previous order to all users who have just logged in. Except, this would be completely irrelevant to two-thirds of your users, because this on-paper personalized offer would in fact be based on the actions of a minority.
The opposite can also occur: you might miss out on potential profits by running a marketing campaign and targeting only a small user segment, despite this campaign being relevant to your entire audience. For example, you may think that only new customers are interested in your annual subscription plan, while, in reality, existing premium subscribers are interested in getting an early renewal at a bargain price.
Besides, personalized offers can reduce the effectiveness of referral marketing. When a user receives an individual discount promo code, they simply can’t share it with anyone else (unlike a mass “25% off everything” offer).
People usually don’t mind sharing their data in exchange for personalization. Salesforce reported that 57% of consumers would trade personal information for personalized offers. And according to Statista, 48% of consumers say it’s fine if businesses use their purchase history to create personalized offers.
At the same time, it is important to pay due attention to the security of user data. Make sure you are completely transparent with your users when it comes to collecting and using their personal information.
So how to avoid common pitfalls and implement mobile app personalization quickly without losing much?
After your first successful experiment, you can start scaling up your personalization to cover more audience segments and app sections.
Today, many mobile users expect not only many features and a user-friendly interface, but also to see personalized content and feel heard and being cared for.
Tailoring offers to each individual customer enables you to achieve greater engagement, retention and brand loyalty, which is a reason to start focusing on the personalization of user experience right away.
Personalization is no easy journey and takes a significant amount of time and effort. High-level personalization with an individual approach to each user is virtually impossible without ML models and CRM systems.
Thankfully, there are tools like MyTracker Personalize that offer ready-made mobile personalization solutions and eliminate the need to hire machine learning experts, pay for expensive storage, process data, train ML models and conduct manual testing.
Read more about our three-month experiment at using MyTracker Personalize models in Hustle Castle, a mobile castle simulator. The personalized offers helped increase ARPU by 23% within the tested group.
