Openly is a service that helps couples show they care. The idea was developed by Sophie Kwok and I as a 9 month project at AC4D. Read the full story.
The first thing users do when they start Openly is take a quiz based on the Five Love Languages. We used an existing framework for this section because a lot of couples already understand Love Languages, or how they best feel loved. The five Languages are: Words of Affirmation, Quality Time, Physical Touch, Receiving Gifts, and Acts of Service. The idea is that way you feel loved may not match your partner, and you may sometimes have to translate your actions to your partner's "language" to understand that you both care.
How do we avoid fostering co-dependence and encourage healthy behaviors?
I had misgivings about relying too heavily on this system. I think it's helpful to understand how you're different, but I can see how the existing system could foster unreasonable expectations. Something like... "you know my Love Language is Acts of Service, but you never help me out around the house. That must mean you don't really love me." This hesitation drove me to figure out how the 30-question quiz for the Five Love Languages works, and how Openly could build on their framework to foster equal partnerships.
First, I looked at the wording the Five Love Language assessment uses for each Language. Each "question" is a paring of two statements representing Love Languages. For example, Acts of Service has a lot more wording like "unexpectedly," "without asking" because this language is about your partner wanting to help you out. Receiving Gifts has a lot of wording like "showing appreciation" "thinking of me." This exercise helped me understand the underlying emotion behind these Love Languages. Acts of Service is about knowing your partner prioritizes your partnership and respects your effort; Receiving Gifts is not about spending money, but about knowing that your partner is thinking about you when you're not together.
Over time, we would like to make Openly learn more about each user's preferences. Those preferences may not even fall along the lines of the Love Languages. For example, in testing with Kim and her husband Brian, Kim tested as having the Love Language "Words." She mentioned during user testing that she wants him to plan trips, cook her dinner--and yes, also write notes to tell her she's beautiful. We believe that means what she really wants is for Brian to take more initiative in the relationship. That doesn't quite line up with a Love Language, but it's a valuable insight.
One way of learning preference is through understanding motivation. Discussions with Sophie and our classmates about how often the app should be notifying users, or how many suggestions users would get led me to researching motivation and personality types. I wanted more choice for our users, maybe three options a day, with the ability to get notifications as many times as you wanted per week. Personally, getting notifications telling me what to do daily would probably make me delete the app. Sophie knew that she would do best with a daily notification stating one thing she could do to help her partner. Neither of these possibilities are wrong. We just have different personalities. Our users will have different personalities. My instinct is that couples using Openly to understand each other will have different personalities from one another, so having a shared couple's setting might not work either.
The profiles from the second assessment influence how Openly "talks" to each user. For instance if Kim and Brian both felt love through Words, but Kim was an Analyst and Brian an Ally, a suggestion might look something like this:
Kim- You know Brian feels love through words. Think of two reasons you love spending time with him, and send him a text or write him a note to let him know you care.
Brian- Write Kim a love note with two reasons you love spending time with her to help brighten her day.
We're asking them to do the same thing. How we're asking them changes very slightly so that the Analyst has more factual explanation, and the Ally has more sentimental appeal.
I would love to test this theory on a large scale to see if we can really motivate people to change their behavior long term. And--even better--to feel good about that change. I know that a lot of apps and wearables in the realm of "quantified self" fail in this area. I believe that's because devices and apps are not yet learning from the data they're collecting about individuals. Openly has a lot of work to do before we get to the point where we can really nudge behavior, but what a fascinating journey it could be!
