Personalization: start with SMALL data
The expectations of customer experience are changing — we’re now used to services like Facebook and Amazon that understand every facet of our life. They know where we live, what we eat and may even know our intimate details better than our own parents. However, when it comes to personalization, it’s easy to get lost in the idea of analyzing massive data sets from hundreds of sources. When it comes to personalization, bigger is not always better — you may not need as much data as you think to start generating results.
The anonymous personalization challenge
Personal details like name, age, and gender make it very easy for you to personalize content and put customers into segments. However, it’s not always easy to convince customers to give those details up. This is especially true for industries dealing with high sales volumes, such as quick service restaurants.
Retailers we’ve spoken to report that anywhere from 50% to 90% of customers will opt not to register if given the choice.
Sure, you can offer incentives like a free sundae on the customer’s birthday to encourage them to register, and using Facebook other social logins can help speed up the process. However, these are ultimately all additional barriers to getting a customer using your app and in a quick service environment a little barrier can be a big problem.
So how do you personalize content if you don’t have any personal details?
Past behavior is a great starting point — analyzing products a customer has looked at, orders they’ve placed and behavior on websites can help you put that customer into a predictive “bucket” or segment. This has historically been hard to do though, as web tracking relies on the shopper using the same browser over repeat visits, and opting to retain cookies on their browser between visits.
This is where a native mobile app is different — because each device is unique you know from the app when that user is browsing the menu or catalog, and what they’re looking at. And if you have limited time offers or mCommerce in the app, then you can also capture data on what they redeem or buy in-store.
By using historic behavior like this, you have an initial data source that can give you an accurate idea of what content and offers to send each user.
But what about a completely unknown customer?
Again, this is a strength of mobile. If you don’t have historic information like app usage or purchase data (and even if you do!) mobile gives you another ace up your sleeve: location. By understanding where a customer is when they’re using your app, you can offer them much more relevant offers.
Location not only tells you which store they are nearby or in. When overlaid with weather data, it tells you what the conditions are like for them right now — whether they’re going to be interested in an ice cream or a hot chocolate with their lunch combo. Add in aggregate data and you can even start to make predictions about who they are or what they’re doing — eg a central city precinct may be largely populated by workers interested in a quick break during the day and restaurant goers with more time and a bigger budget at night.
It’s the location element that is perhaps the most powerful tool in reaching anonymous users. It goes beyond collected historic data and brings a real-time element; you can deliver more relevant offers right from the initial app install.
What if customers don’t want you to know where they are?
There’s always going to be some customers who are reluctant to share their location. While many app users (particularly millennials) are now comfortable with sharing location data with a reputable brand, there are still people — about 20% of mobile users keep location turned off — who need a little more encouragement.
Offering location based functionality is one way of giving customers a benefit for sharing location data: a store locator gives customers a clear reason to enable location services in the app. More advanced and more compelling reasons to share location will generally have bigger impact — offering a loyalty reward or other bonus for checking in at a store when visiting, and requiring customers to share location to do this.
Ultimately, it’s a value exchange
If a customer sees additional value, they’ll be happy to share more information. The same principle applies when asking the customer to log in. Rather than requiring them to log in straight away to start using your app, why not make it really easy to install and start using it, then start showing them some great offers relevant to their current location and weather. Once they’re using the app and seeing that value, then offer them some additional value for registering, for example a loyalty card or a one-time contest.
Brands need to realize that the flip side of personalization is that the relationship dynamic changes — you’re building a much deeper connection with customers and they expect a much deeper level of personalization in return.
One of the biggest mistakes brands make with mobile apps is relying on large amounts of data to create a personalized experience. Start with the easiest and most accessible data, information you can collect without requiring more input from the customer. Build a great experience and use that to start building a relationship.
Otherwise, it’s like going on a first date and talking about your future children’s names — it’ll scare away 99% of your prospects.