What Digital Marketers can Learn from Behavioral Economic Research in the Developing World

Digital marketing analytics and data collection present a great opportunity for modern marketers. We can answer just about every question about human behavior in our online environment – where you acquired them from, how they use your site, when they’ve visited, etcetera. But even with all of this data, determining why someone did something is still an educated assumption at best.

Luckily there’s a wealth of research and data at marketers’ disposal that they can employ to complement their digital data, and create a more complete customer persona. Behavioral economics, in particular, offers a unique resource for marketers who want to take their targeting strategies to the next level .

Behavioral Economics

Behavioral economics is a fascinating field because it focuses on two aspects: What the sensible, prudent choice would be in a financial situation; and what people actually do instead. A nonprofit organization pioneering research in this field is Innovations for PovertyAction (IPA). Hearing that example, you’re probably thinking “What on earth does research on financial behavior in the developing world have to do with digital marketing in a first world country?”

The answer is that people are equally as bad with money and irrational about their spending habits in the first world as the developing world. And on the other side of the coin, sometimes the best choice for an individual is not always what a company might imagine.

Translating Third-World Crop Insurance into Conversions

Take this study for example, which offered farmers in Kenya three options for buying fertilizer: A standard offer; a discounted offer where the price was reduced 30%; and a “harvest deduction offer,” where farmers would pay full-price, but a significant portion of was paid after harvest. 72% of respondents chose the third, Harvest Deduction offer.

Now here’s the dreaded question: why? The researchers posited two ideas.

1.     The farmers have a present bias, meaning they give more weight to costs closer to the present time, and don’t feel the loss as much when considering a further future payment;

2.     Farmers did not have enough lump sum capital to make the full purchase at the outset without selling crops, even with a 30% discount.

So how does selling agricultural insurance in Kenya relate to selling products online? Take, for example, a segment of customers who have been buying from your site for a long period of time, but their purchases are less frequent compared to other customers, and they also spend less money per order. This segment also tends to make wish lists if your site has that feature, or they put items in their cart and leave them there for long periods of time without ever even beginning the checkout funnel. Just like the farmers, they know they want to buy from you, but they also know when it is prudent for them to actually finalize the purchase.

This is valuable information. Now you can send them discounts via email to speed up and encourage the purchase process; you can launch a CPI remarketing ad campaign targeting this segment to keep ad costs low and conversions high; and if you notice a segment like this comprising a large swatch of your customer base, you may want to consider introducing payment plan options like the researchers did in Kenya. This would make the customer buy earlier, possibly buy more frequently, and greatly increase their lifetime value.

Lessons in Direct Marketing from Ghana

Another IPA study in Ghana tested whether text message reminders straight to peoples’ cell phones increased their rate of completion for malaria treatment. The results are significant, but not staggering. There was a 5% increase in completion in the treatment group. Two different messages were used – a short one, and longer one with an additional warning – and there was no statistically significant difference between the two.

However, the short text message reminder more than doubled the completion rates amongst women, yet had no significant effect on completion rates amongst men. It’s also worth noting that participants who attended private clinics had a 14% increase in completion compared to participants who attended public hospitals and the control group.

Again, how does this relate to digital marketing? Simply put, it reinforces that profiling is not only ok in the marketing world, but it has more value than marketers may even give it credit for. Granted there is more research needed and every industry is different, but taking medication for a sickness you want to get rid of is a pretty convincing control.

According to this research, a direct marketing message reminding someone of something they want to do, that also provides a direct reminder of next steps to take, can be very effective with women, but not so much with men. Also, the private clinic participants could be considered your segment of reliable customers – they’re looking for you to reach out to them, and they’re more ready than their counterparts to take action.

In short, to get ahead and stand out in the marketing world, you can’t limit yourself to marketing data. Yes, digital data is nearly infallible when collected correctly. And no, spending your free time reading up on behavioral economic theory may not be your ideal Friday night. But if you want to get closer to answering “why?” you’re going to have to look further than Google Analytics. 

An Introduction to Digital Psychology

Digital psychology, also known as web psychology, is the study of human behavior and rationality in the context of a digital environment. And if you have an analytics account, you’re already collecting and contributing to the exponentially expanding pool of digital human behavior data out there. If you're training up in your skills as a digital data analyst, then you're already in possession of the tools needed to succeed. You can learn more about this specific skill set in this post by Greg Benson

A great example of a company using digital psychology, and in an industry that has been greatly affected by the digital age, is GrubHub. GrubHub uses social proof, or informational social influence, to help guide your food buying experience.

The first thing you notice about the site is its design: the home screen is comprised of tiles with ample blank space between them. This is a design that is commonly associated with social networks, as if each restaurant were a post. The first piece of information after the restaurant’s name is their star rating. This star rating is also the only part of the tiles that produce an informational pop-up when hovered over, giving a written review and links to more reviews. Creating a familiar aesthetic and putting the opinions of hundreds of individuals front-and-center is a truly social take on the digital food ordering experience.

Another brilliant use of digital psychology is the ubiquitous Amazon. This retail behemoth had a stroke of genius when they launched Amazon Prime. Prime isn’t just a proven commitment device – it’s a commitment device that people pay one hundred dollars for. And free shipping removes a mental barrier between a customer and purchasing, a pairing of two digital psychology tactics that have paid out (and are still paying out) increasingly large dividends for Amazon.

A lot of digital psychology strategies sound obvious at face value. But the real trick comes in executing the strategies in elegant, powerful ways that make purchasing goods online a seamless, nearly habit-induced action by the user.

The Effects of Emotional Contagion on Social Marketing

Facebook has a section in their privacy and data sharing agreements that some people will find awesome, and most people will find terrifying: If a researcher has funding to replicate a published study, Facebook will give them open access to their user data. How could a researcher resist access to hundreds of millions of people’s psychologies, buying behaviors, schedules, and pictures, all untainted by a laboratory setting? There is some pioneering observations into the potential scientific use of social media data in predictive analytics, such as this post from Carlo Fiore.

The answer is that researchers seem somehow uninterested in Facebook’s proposition. It was a struggle to find any studies that took advantage of this wealth of data, and an even bigger struggle to find results that can be applied to digital marketing.

But I did find one with interesting implications. The study DetectingEmotional Contagion in Massive Social Networks by Lorenzo Coviello, Yunkyu Sohn, Adam D. I. Kramer, Cameron Marlow, Massimo Franceschetti, Nicholas A. Christakis, and James H. Fowler, used Facebook’s user data to determine whether or not negative and positive sentiments spread throughout users’ networks, and if so, which one was more contagious.

The way the researchers designed the experiment was simple and brilliant. They would find a highly populated area that was experiencing rainfall. They then monitored the friends of the people in this area, but only the friends who lived in a location where it wasn’t raining.

The researchers then compared the mood histories of users in both locations, and determined that rain increased negative posts and decreased positive posts by a statistically significant amount. In addition to this, every positive post increased the likelihood of a friend posting something positive, and the same with negative posts, regardless of whether or not the friend was in the rainy area.

There are already marketing campaigns that leverage weather targeting to deliver ads. But incorporating research like this into like-minded campaigns can yield even greater opportunities for personalizing ads. If your users are likely to be feeling positive, encourage that with your content. If your users are in conditions that are likely to make them feel sad, identify the conditions that might be having this effect, and try to counteract them with your messaging. People will be far more forgiving of you violating their privacy and manipulating their emotions if you do it all to help bring a smile to their face.

Status and Future of Marketing Analytics in the Food Industry

The digital era hit the food industry in a heavy way. What used to be an experience that consisted of trying some place you’ve never heard of has become a detailed investigative effort every time you want a chicken nugget. Now you can check reviews, ask friends and acquaintances for their opinions, photo journal your entire dining experience, gauge your daily calorie intake, and spew venom on Yelp about the shitty service/food/cocktails/selfie-unflattering-bathroom-lighting before you’ve even tipped the waitress. Here are three major ways the food industry is changing in the digital world, and what trends it’s heading for.

Word of Mouth: Social Media

The culinary world has always been akin to the fashion world. Restaurants gain and wane in popularity as trends do, with influencers and publications like Zagat directing the peaks and valleys. But something that could influence you more than an influencer? Someone you know, whose opinion you trust, making a recommendation of a great place to eat.

The digital era has taken this driving force and amplified it. Social media platforms give everyone a means of speaking directly to each other in detail, and in groups, about experiences they’ve had at various dining establishments. Beyond that, people can post whatever their thoughts are and effectively project their opinions out into the ether for anyone to pick up.

The prime example of this, of course, is Instagram. Not only is it the go-to platform for posting pictures of your dinner (or, god help you, brunch), but it has the advantage over platforms like Facebook in that it allows users to easily reach outside of their networks. Using tagging methods like hashtags allows anyone to find your posts as long as they’re searching for it - friended, followed, or not. For restaurants, this turns every customer into a potential free and potent advertisement.

Food critics are becoming necessary only for the kind of establishments that NBA players book months in advance. For everyone else, there’s everyone else.

Word of Mouth: Comments

Everyone knows that, for the most part, the collective comments sections of the internet make-up the real Dark Web. They’re vitriolic, unverifiable, and often downright nasty in both senses of the word.

But they’re also great for SEO. And they’re also the best way for website users to actively engage with an otherwise static webpage. And although they’re all strangers, a comments section is comprised of people who cared enough to come back and comment on a product or service that is currently in your headspace right at the consideration stage of the purchasing funnel.

This is how Yelp changed the game. Crowd-sourced comments and critiques of restaurants and individual dishes has become the vanguard of deciding what to get for dinner tonight. The advice of a close friend carries a ton of weight. The aggregated and averaged advice of 4,000 overly passionate strangers will do in a pinch.

Online Ordering

The most disruptive culinary innovations in recent years have been in online ordering. Restaurants that were already based mostly off of deliveries, like Domino’s, quickly adapted to have strong online presences. Customers could build online profiles to expedite checkout, take their time browsing menus and deals, and never have to engage with another person. Companies also started integrated marketing strategies. They started offering users low-priced items in between order pages. When you logged in, they would remind you of past orders, or recommend new ones based on your history.

But that’s old news at this point. The real innovation worth watching was started by companies like GrubHub. Now restaurants that have never delivered before and never had delivery fleets can deliver. Those wings you always stagger to get with your friends at 2 in the morning? Now you can order them slouched on a couch with Netflix on.

Taking that a step further is UberEats. Uber started as a ride hailing service that millennials adopted at an alarming rate, and it quickly became ubiquitous. My (unconfirmed, unresearched) guess is that Uber drivers were reporting a high volume of young drunk people getting rides from bars, to restaurants, to home. So Uber took the rides they were already providing, added a significant number of additional people who were already home, and slapped a delivery charge in the middle. And we’re all eating it up.

The Takeaway? We Aren’t Going Out Much Anymore.

What all of this seems to be leading to is dominance of preference given to ultimate personalization, over other considerations like the collective desire of a group. We ask our friends what they would recommend. We check reviews and offerings. We compare prices. We carefully select what we want. We order it directly to our homes. Then we eat it alone.