Machine learning is being increasingly employed to help consumers find a better love match
Once upon a time, meeting a partner online was not seen as conducive to a happily ever after. In fact, it was seen as a forbidden forest.
However, in the modern age of time poor, stressed-out professionals, meeting someone online is not only seen as essential, it can also be considered to be the more scientific way to go about the happy ending.
For years, eHarmony has been using human psychology and relationship research to recommend mates for singles looking for a meaningful relationship. Now, the data-driven technology company is expanding upon its data analytics and computer science roots as it embraces modern big data, machine learning and cloud computing technologies to offer millions of users even better matches.
eHarmony's head of technology, Prateek Jain, who is driving the use of big data and AI modelling as a way to improve its attraction models, told CMO the matchmaking service now goes beyond the traditional compatibility into what it calls 'affinity', a process of generating behavioural data using machine learning (ML) models to ultimately offer more personalised recommendations to its users. The company now runs 20 affinity models in its efforts to improve matches, capturing data on things like photo features, user preferences, site usage and profile content.
The company is also using ML in its distribution, to solve a flow problem through a CS2 distribution algorithm to increase match satisfaction across the user base. This produces offerings like real-time recommendations, batch recommendations, and something it calls ‘serendipitous’ recommendations, as well as capturing data to figure out the best time to serve recommendations to users when they will be most receptive.
Under Jain’s leadership, eHarmony has also redesigned its recommendations infrastructure and moving over to the cloud to allow for machine learning algorithms at scale.
“The first thing is compatibility matching, to ensure whomever we are matching together are compatible. However, I can find you the most compatible person on the planet, but if you’re not attracted to that person you are not going to reach out to them and communicate,” Jain said.
“That is a failure in our eyes. That’s where we bring in machine learning to learn about your usage patterns on our site. We learn about your preferences, what kind of people you’re reaching out to, what images you’re looking at, how frequently you are logging in to the site, the kinds of photos on your profile, in order to look for data to see what kind of matches we should be giving you, for far better affinity."
As an example, Jain said his team looks at days since a last login to find out how engaged a user is in the process of finding someone, how many profiles they have checked out, and if they regularly message someone first, or wait to be messaged.
Read more: 9 machine learning myths
"We learn a lot from that. Are you logging in three times a day and constantly checking, and are therefore a user with high intent? If so, we want to match you with someone who has a similar high intent," he explained.
“Each profile you check out tells us something about you. Are you liking a similar kind of person? Are you checking out profiles that are rich in content, so I know you are a detail-oriented person? If so, then we need to give you more profiles like that.
“We look at all these signals, because if I present a wrong person in your five to 10 recommended matches, not only am I doing everyone a disservice, all of those matches are competing with each other."
Jain said because eHarmony has been operating for 17 years, the company has a wealth of knowledge it can now draw on from legacy systems, and some 20 billion matches that can be analysed, in order to create a better user experience. Moving to ML was a natural progression for a company that was already data analytics heavy.
Read more: 4 data analytics trends that will dominate 2018
“We analyse all our matches. If they were successful, what made them successful? We then retrain those models and assimilate this into our ML models and run them daily,” he continued.
With the skillsets to implement ML in a small way, the eHarmony team initially started small. As it started seeing the benefits, the business invested more in it.
“We found the key is to define what you are trying to achieve first and then build the technology around it," Jain said. "There has to be direct business value. That’s what a lot of businesses are getting wrong now.”
Machine learning now assists in the entire eHarmony process, even down to helping users build better profiles. Images, in particular, are being analysed through Cloud Vision API for various purposes.
Read more: Big data analytics: The cloud-fueled shift now under way
“We know what kinds of photos do and don’t work on a profile. Therefore, using machine learning, we can advise the user against using specific photos in their profiles, like if you’ve got sunglasses on or if you have multiple people in it. It helps us to assist users in building better profiles,” Jain said.
“We consider the number of communications sent on the system as key to judging our success. Whether communications happen is directly correlated to the quality of the profiles, and one the biggest ways to enhance profiles are the numbers of photos within these profiles. We’ve gone from a range of two photos per profile on average, to about 4.5 to five photos per profile on average, which is a huge leap forward.
“Of course, this is an endless journey. We have volumes of data, but the business is constrained by how quickly we can process this data and put it to use. As we embrace cloud computing technology where we can massively scale out and process this data, it will enable us to build more data-driven features that can improve the end user experience."
Follow CMO on Twitter: @CMOAustralia, take part in the CMO conversation on LinkedIn: CMO ANZ, join us on Facebook: https://www.facebook.com/CMOAustralia, or check us out on Google+:google.com/+CmoAu
In our fifth and final episode, we delve into the relationship between retailer and supplier and how it drives and influences launch marketing strategies and success. To do that, we’re joined by Campbell Davies, group general manager of Associated Retailers Limited, and Kristin Viccars, marketing director A/NZ, Apex Tool Group. Also featured are Five by Five Global managing director, Matt Lawton, and CMO’s Nadia Cameron.
OK, so you’re probably thinking: “Here comes another article to badger me about living in my bubble.” And also, “I bet this bubble-bashing piece will go on to explain how I can achieve better results through some heady dose of new life experiences, new routines and annoyingly different opinions on social media.”
Every day, there are new predictions and studies on the future of work, the state of the economy and the unfolding global pandemic. All of which creates uncertainty and heightens the imperative of effective leadership.
I've worked in brand and marketing for more than 20 years. But there’s one area where I’ve found myself going around in circles and I must admit I'm becoming increasingly confused.
Great read. I agree that it should be a perfect balance between interacting with your customers and knowing your brand. As a business, yo...
Caroline Scott
7 ways CMOs can improve their customer engagement game
Very true. Team development helps improve collaboration among the team members. I was able to improve my team's collaboration skills by t...
Quent Sinder
Why empowering others can help make you a great leader
CRM is a very good software that can help you succeed in your business. In my company, this system has allowed me to improve customer rel...
Anna Janicka
Sensis rebrands to Thryv and brings business software to Australian SMBs
AI Leasing Assistants have finally arrived for the multifamily industry. With so many to choose from it can be hard to figure out which i...
Alice Labs Pte. Ltd.
CMO's top 8 martech stories for the week - 6 May 2021
Nowadays, when everything is being done online, it is good to know that someone is trying to make an improvement. As a company, you are o...
Marcus
10 lessons Telstra has learnt through its T22 transformation