Too many leaders take an incomplete approach to understanding empirical patterns, leading to costly mistakes and misinterpretations. As we have discussed before, one extremely common mistake is interpreting a misleading correlation as causal. We’ve advised countless organizations on the topic. We’ve written research papers, managerial articles, and even a book dedicated to the power of experiments and causal inference tools — a toolkit that economists have adopted and adapted over the past few decades. Yet, while we are deep believers in the causal inference toolkit, we’ve also seen the reverse problem — leaders who overlook useful patterns because they are not causal. The truth is, there are also times when a correlation is not only sufficient, but is exactly what is needed. The mistake leaders make here is failing to understand the distinction between prediction and causation. Or, more specifically, the distinction between predicting an outcome and predicting how a decision will affect an outcome.
Consider a manager that is struggling with the following question: Should I subsidize college degrees for my employees? She might start by examining the relationship between college degrees and productivity. Yet, even if she sees a positive association between college degrees and productivity, it is hard to know — without further analysis — whether this relationship is causal. After all, there are likely to be other underlying differences between people with and without degrees. And offering education subsidies to the degree-less employees won’t make them identical to the other employees who already have a degree. She would need an experiment, or natural experiment, to better understand whether this relationship is causal.
Now, suppose the same manager was grappling with a slightly different question: Should I hire more college graduates? She might again look at the correlation between college degrees and productivity to consider whether she’d hire more productive workers by tweaking hiring to place more weight on a degree. In this case, the correlation is useful — since it is helping to predict who will be productive, even if it says nothing about whether the degree is causing productivity.
A subtle but critical difference exists between these two questions. “Should I hire more college graduates?” is a prediction problem. “Should I subsidize college degrees for my employees?” is a causal inference problem. In the former, she is trying to assess whether college degrees are predictive of productivity. In other words, are the kinds of people who get college degrees good employees? In the latter, she is trying to determine whether college degrees cause higher productivity.
This distinction is critical for decision makers: When considering hiring employees with a college degree, the manager needs predictive tools, which can range from basic correlations to more advanced machine learning algorithms. She might not need to know whether degrees are having a causal effect (or if, instead, the kind of people who get college degrees also happen to be productive employees). When considering subsidizing college degrees for her employees, however, understanding whether it’s the actual college education that causes higher productivity should be her core question. To successfully determine whether degrees will help improve current employee performance, she needs the tools of causal inference, such as experiments or natural experiments, which are focused on understanding the causal impact of making a change.
Here we provide examples of common causal inference and prediction problems. We draw out key distinctions between the two types of problems and point to different tools leaders need when confronting each.
Common causal inference problems
Managers regularly face decisions that involve thinking through the causal impact of different options. Will hiring consultants improve our company’s productivity? Will higher wages reduce turnover? Will advertising on social media draw in new customers?
These questions have all been answered using the causal inference methods from social science. For instance, economists Emma Harrington and Natalia Emanuel, in conjunction with a large tech company, examined wages within the company’s call centers and warehouses. In 2019, the company increased pay for warehouse workers from $16 an hour to $18 an hour. Looking at the timing of the pay raise, the researchers were able to see the effect of higher wages on productivity using a difference-in-differences approach. They found that the raises not only increased productivity, but also that a $1 increase reduced the chances an employee would quit by 19%. As it turns out, it was profitable to increase wages, as the pay hikes more than paid for themselves through the productivity boost and decline in turnover.
As a second example, consider a recent analysis by Brett Gordon, Florian Zettelmeyer, Neha Bhargava, and Dan Chapsky which looks at advertising campaigns run on Facebook. Looking at 15 U.S.-based advertising campaigns consisting of roughly 1.6 billion advertising impressions, the researchers compare the estimates of the impact of advertisements on Facebook from experiments to the estimates from non-experimental correlations. The team found that the non-experimental correlations between advertisements and purchase intentions were misleading, as advertisements are targeted and tend to be shown to users who are already inclined to purchase a product. For instance, laundry detergent ads are going to be shown to people who are already inclined to buy laundry detergent even in the absences of the ad. The authors then investigated different non-experimental approaches to controlling for characteristics of users, and found that the correlation remained misleading despite the controls. Even more advanced statistical controls didn’t eliminate this ‘selection bias’ problem. This is because selection bias is especially severe in the context of online advertisements, where advertisements are heavily targeted and where effects tend to be small on a per impression basis, which means that even small amounts of bias can lead to very misleading estimates overall. In that context, experiments can be a powerful way to overcome selection bias and to identify the causal impact of advertisements.
A third example comes from the world of financial products, where one of us (Dean), with colleagues Jeremy Burke, Julian Jamison, Kata Mihaly, and Jonathan Zinman, ran a study with a credit union in St Louis. It looked at a popular “credit builder” loan product designed to help those who wanted to establish a credit history do so. Indeed, if you just looked a correlation, you’d find that people who availed themselves of the product designed to build credit scores did go on to build credit scores — success! But because the credit union had randomized the offers, they found plenty of people similar to those successful clients who hadn’t been offered that product also went on to build good credit scores on their own. Again, we have problem of the college degree correlation — the people who are the type of people who want it, tend to be the type to be successful. It wasn’t the product that did it, but the correlation might make you think it was.
These are just three of many examples of how the causal inference toolkit can answer critical questions in areas ranging from operations to strategy to marketing.
Common prediction problems
If your employees or customers are a self-selecting group, does that mean you’re out of luck? No, finding out a credit improvement product seemed to lead to no increase in scores, might be interpreted as a failure of the product, but it’s not a failure of information. Recall that a user’s decision to use the product turned out to be quite predictive of whether their score would improve. If you are the bank, that is information you can use. For example, you may want to use similar information to assess credit risks. Banks might be more willing to give credit to individuals with low credit scores who elect to use a credit improvement product compared to individuals who don’t use the product. The reason is simple: Using the product is predictive of future behavior, even though it is not causing the behavior.
Managers in all industries regularly face decisions that involve making predictions.
Machine learning and artificial intelligence are extremely valuable in these contexts. Our own research has documented the potential for algorithms to lead to more efficient hiring and promotion processes in areas ranging from teachers to police officers. Recent work has further explored these ideas, and found that algorithms have the potential to increase both efficiency and equity of hiring. For instance, consider a recent paper by economists Danielle Li, Lindsey Raymond, and Peter Bergman, which examines the value of using an algorithm to screen resumes — with data on roughly 90,000 job applications to a Fortune 500 firm between 2016 and 2019. Comparing multiple algorithms to human decision makers, the researchers found the algorithms helped to identify better candidates in the screening than the people did, leading to a higher likelihood that the candidates were hired. Moreover, when carefully designed, the algorithms led to both higher quality candidates and more demographically diverse candidates. But, to get there, the organization needed to realize that there is an element of prediction in hiring and needed to be clear about what its hiring goals were are.
As a third example, suppose that you were to see a correlation between a given year’s most popular cuisines in Boston and the prior year’s most popular cuisines in New York. Even if the link is not causal, the correlation is valuable. For instance, it can be insightful for restaurants that are looking to innovate in their menus. One of us (Mike) has seen this type of question come up in his work with Yelp, where it is possible to look at large scale data sets to answer this type of question. This work has helped to ways in which data from tech companies can shed light on the evolution of economic activity. For instance, Yelp data can help to provide insight into the ways in which gentrificationaffects different types of businesses. It can also help to predict changes in economic activity. More broadly, data from tech companies has been one important new source of information — and has now been widely used for both causal inference and prediction problems.
Choosing the right machinery
“We are drowning in information but starving for wisdom.” This quote, from biologist E.O. Wilson, captures the essence of the modern business ecosystem. The world is awash in data. And advances in data analytics over recent decades have the potential to improve managerial decisions in virtually all sectors and for a wide range of problems. A large body of economics and statistics literature has explored the ways in which artificial intelligence has reduced the cost of making predictions, in settings ranging from hiring to investing to driverless cars. In parallel, the development of causal inference tools has been recognized in the 2019 and 2021 Nobel Prizes in Economics. Both are important for business decisions.
Yet, leaders too often misinterpret empirical patterns and miss opportunities to engage in data-driven thinking. To better leverage data, leaders need to understand the types of problems data can help solve as well as the difference between those problems that can be solved with improved prediction and those that can be solved with a better understanding of causation.
Elon Musk said Sunday he “somewhat agonized” over the font designs for his companies Tesla and SpaceX.
The billionaire businessman added he “loves fonts” and has tweaked the logos over the years.
He revealed the SpaceX logo also holds a hidden meaning, representing a rocket’s arc to orbit.
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In a series of Sunday tweets, Elon Musk said he “somewhat agonized” over his choice of fonts for his businesses and revealed a hidden meaning behind the SpaceX logo.
Responding to a tweet about serif and sans-serif fonts, the billionaire businessman took a break from posting cryptic memes and discussing politics to say he loves fonts and put significant consideration into how his companies are presented to consumers.
“I somewhat agonized over the Tesla & SpaceX font design (love fonts tbh),” Musk tweeted. “There are some similarities, particularly use of negative space. We’ve made many little tweaks over the years.”
The Tesla logo — a T-shaped design with a custom, sans-serif font spelling out the brand name — is meant to resemble a cross-section of an electric motor. The SpaceX logo, written in a similar font with an extended X, references the reusable rockets made by the company.
“The swoop of the X is meant to represent the rocket’s arc to orbit,” Musk tweeted.
Other business logos have also held hidden messages: Baskin Robbins, a chain that sells 31 flavors of ice cream, has a secret ’31’ hidden in the letters of its logo. Likewise, Amazon’s arrow logo is meant to represent a smile, while the circular ‘B’ logo for Beats by Dre represents a person wearing the popular headphones.
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The internet has revolutionized the business world and changed how we conduct business. Any business that aims to increase its visibility and boost profit needs to pay much attention to top ranking factors, including local SEO — which introduces the topic of the local search algorithm.
Local SEO is one of the top practices that help boost a business’s visibility and generates more sales.
However, achieving better local SEO rankings is not a walk in the park, especially due to increased competition. To appear higher on local results, businesses and marketers need to understand how the local search algorithm works.
Knowing this helps guide the steps for improving rankings in the local pack.
The competition gets stiffer as more businesses open and optimize for local searching. Besides, Google is updating its algorithm consistently, meaning only businesses that can keep up with these updates can appear at the top of local search results.
Luckily, you have come to this post as this article looks at everything you need to know about Google’s local search algorithm and what you can do to get that top spot in the local pack.
Understanding the local search algorithm
Google aims to provide the best results that match a specific local search query. It constantly updates the local search algorithm to determine which business to rank on top of local search results.
Ideally, Google wants to provide local content that is relevant and valuable to users. As with search engine optimization, keyword stuffing cannot give you that top spot in local search results.
SEO specialists and marketers should consider Google’s local search algorithm updates and make the necessary changes to rank higher. Failure to consider these updates means losing your local search presence, resulting in fewer leads and conversions.
Local algorithms check the Google My Business (GMB) listings to determine where to rank a business in local search rankings.
Ideally, Google’s local algorithm ranks businesses with information that matches a searcher’s query. And the higher a business ranks in local search results, the more chances a potential customer will click on it.
This post looks at the three major pillars that determine local search results to better understand the local search algorithm: proximity, prominence and relevance.
Of course, other factors make up Google’s local search algorithm, but since we cannot identify all of them, we’ll focus on the most crucial ones in this post.
By understanding these pillars, marketers can better position themselves for local search success.
Proximity is one of the major ranking factors when it comes to local search. That means the distance between a business and a searcher is a ranking factor in local search.
When a searcher searches for something, Google considers how far the searcher is from the location of the term they use in the search. When a searcher doesn’t specify the location, Google calculates the distance based on the information they have regarding their location.
Ideally, Google aims to provide the most relevant results to a search query. For instance, why would Google provide a list of coffee shops in Los Angeles if the searcher is searching from Colombia?
That would be irrelevant local search results that won’t benefit the searcher.
Unfortunately, while proximity is a major local search pillar, it’s one of the factors that businesses have little control over. After all, you cannot change where your business is located, right?
You can only ensure your business location is as clear as possible, so that it appears for related nearby queries. Here are steps you can take to achieve this:
Claim and verify the Google My Business listing
Ensure local listings are accurate and optimized for local products or services
Get the Google Maps API Key and optimize for your location and routes
Set up your profile correctly (for Service Area Businesses) to avoid violating Google’s guidelines
Users can perform several types of local searches, including:
Users will perform geo-modified searches when they are planning to visit somewhere. For instance, a searcher in Los Angeles planning to visit Toronto, Canada, may search for a “coffee shop in Oakville.” The results will differ from if they searched for “coffee” while physically in Oakville.
To be specific, geo-modified searches are mainly based on relevance and prominence as opposed to proximity when a user searches for something when outside the city included in the search.
Searchers perform this type of search when looking for something around them. For instance, a user in Los Angeles performing a local search for “coffee.”
Ideally, the user only needs to search for something and is shown results based on proximity. They will get the results that are closest to them.
“Near me” searches
“Near me” searches have been so popular in recent years. Although their popularity has significantly declined, users still perform this type of search when looking for something locally.
For instance, some users could add “near me” when searching for a coffee shop, hoping to get the most relevant results near them. As we’ve stated, this trend has lost popularity because when you perform a local search, you are searching for something near you.
It is not necessary to add “near me” to what you’re searching.
Prominence refers to how important Google thinks your business is, which gets factored into the local search algorithm.
In other words, it refers to how well a business stands from the rest in various aspects, including directories, links, reviews, mentions, among other things.
If search engines view your business as trustworthy and credible, they will likely show it on top of related search query results.
The local search algorithm views businesses/brands with a stronger online prominence as credible and trustworthy. Some of the factors that determine prominence include:
A local citation is the mention of a business’s information online. The mention can include the partial or complete name, address, and phone number (NAP) of a local business.
Citations are an excellent way for people to learn about local businesses and impact local search results.
A business with high-quality citations can rank better in local search results, although businesses must continually manage citations to ensure data accuracy.
Backlinks play a crucial role in local business prominence. Gaining relevant backlinks from high-quality sites is an excellent way to build a business’ online reputation.
If you’re trying to outrank your competitors without much success, your backlink profile could be the reason.
In that case, you should check your competitor’s backlinks and compare them with yours. When doing this, pay attention to the number and quality of their backlinks.
As a rule of thumb, aim to have high-quality local backlinks pointing to your site to improve your page’s authority.
Next, you need to pay much attention to reviews to improve local prominence. Many customers look at a business’s online reviews before deciding whether to engage more with the business or not. Besides, many positive online reviews can increase a business’ ranking factors.
Consider this scenario. A potential customer is looking for a pub around Oakville. When they perform a search, they are presented with two results: one with over 100 reviews and another with less than 10 reviews.
Which business do you think the searcher would trust? The one with 100 reviews, obviously.
As with search engines, customers need to trust a business before they decide to do business with it. Similarly, search engines can view online reviews and analyze them to determine a business’s online prominence.
That said, here are strategies you can use to boost your online review signals:
Have a strategy
You won’t have a strong online prominence if your products or services are not of a high standard. So, the first step to having many great reviews is to develop great products and services.
After that, develop a strategy to encourage your happy customers to leave honest but valuable reviews of their experience doing business with you to help boost your online reputation.
Monitor and manage the reviews
Having many reviews is one thing; you need to develop a plan to engage with your customers for better results. Responding to reviews shows people that you care and are genuine about your products and services.
People will avoid businesses that don’t respond to customer reviews (whether positive or negative).
Search engines, too, can tell whether you engage with customer reviews or not and will use the information to determine where to rank on local search results.
When responding to online reviews, pay special attention to negative reviews and how you respond to them. While no business likes getting negative reviews, how you respond to them can positively impact your business — respond positively to turn the negative reviews around.
As earlier stated, Google wants to provide the most relevant results to a local search query. This key ranking factor will determine a business’s position in local search results — how well does a local business match a search query?
Even if your business ticks the above pillars (prominence and proximity), if the content on your page isn’t well structured and doesn’t cover the topics that a searcher is looking for, you won’t appear on top of local search results.
Here are factors that businesses should consider to create a relevant listing:
Local page signals
Local listing categories and attributes
Social posts and responses to online reviews
Local listing signals and categories
A business GMB listing and category can impact its relevance score for local searches. As such, complete your business profile carefully and continually add quality content to the web page to ensure it is relevant for proximity searches.
More specifically, ensure that all information on all listing pages, including Yelp, Bing, and Google, is complete and accurate. Aside from these factors, here are two crucial features you should pay attention to:
Selecting the right categories for your local business listing is among the crucial factors for ranking locally. With over 4000 GMB categories, you want to choose categories that best describe your business — ensure they are relevant and specific.
Here are guidelines to follow when selecting a category:
Describe your business as opposed to your services
Be specific to minimize competition
Reduce the number of GMB categories to describe your business better
Without a proper description, users won’t know what your business is about. This section is about adding an introduction to your business so that customers and search engines can know more about your business.
However, don’t use this section for marketing your business. Just give users and search engines descriptive info that can help determine whether your business matches their needs.
Local page signals
Another way a business can improve its standing in the local search algorithm is by optimizing web pages for specific keywords. For multi-location businesses, it’s essential to have separate, localized pages for each location, with relevant information and contact details for customers to reach you.
Performing competitor research is advisable to determine what terms or keywords to use for a specific query. Here are top on-page signals to consider when trying to gain relevance for a given topic:
Keyword research — Before creating local content, you need to find keywords that matter to your business. Perform keyword research to determine highly relevant keywords with high intent. When finding relevant terms to use in your content, base your research on the customer perspective; think about what they search for and the type of content they are looking for.
Create local content — After finding the right keywords, it’s time to create your content. Google values the quality of content more than the length of the content, so keep this in mind when creating content. Another crucial thing to pay attention to is localizing the content. For example, you can create content on local news and events or use your city’s name within your content.
The goal is to create a connection between what’s happening in your local area and your business. Also, use pictures with your specific geolocation to increase your content relevance.
Creating quality and relevant content is only the start. You need to optimize your content for on-page signals so local search algorithms can discover and rank them better. Here’s how you can optimize your local content for on-page signals:
Meta descriptions — Include keywords in your meta descriptions to encourage searchers to click through and increase visibility
Title tags — Title tags are some of the factors that search engines use to determine where to rank content. Incorporating keywords naturally in your title tags can help boost local rankings
Image tags — Another way to improve local rankings is by including relevant keywords in your image tags. Including geotags also comes with an added advantage
Headings — Users and Google value pages with clear structures. Consider creating headings within your content to capture readers’ attention and encourage them to read on. However, ensure your heading tags describe the content that comes after them well. Also, include keywords in your heading tags to help search engines understand them and their importance.
Off-page local signals
Gaining high-quality backlinks is a great way to boost credibility and trust. Backlinks refer to external links from another website to your site. Aim to have more high-quality backlinks to boost your website authority.
Ideally, having many quality backlinks shows search engines that your website or page is credible and trustworthy, which boosts the chances of ranking it higher in search engine results.
Guest posting is one of the best examples of link-building strategies you can use. Finding great guest posting opportunities provides an excellent opportunity to share your content to a new but relevant audience, which helps boost your website authority.
Another strategy you can use is to create longer and better content than what is already available on the web. When your content is high quality and relevant, it will be easier to get high-quality backlinks.
Review and social signals
Online reviews can also help boost relevance for your local business. Aim to get as many positive reviews from your happy customers as possible.
Remember, when customers perform a local search, they get not only the relevant businesses but also reviews related to the search. The more positive reviews a business has, the higher chances a potential customer will do business with them.
Closing thoughts on the local search algorithm
Ranking on top of local search results can seem daunting, but it shouldn’t when you know the vital things to focus on. As you have seen above, the local algorithm is based on three pillars: relevance, proximity, and prominence.
Of course, other factors determine local search rankings depending on your industry and competition.
Ah, email. Why did you send my friend’s birthday party invite to my spam folder? Why do you make it so easy to archive an email when I don’t even know what that means? Why are you … blue now … Gmail?
Email is a necessary evil. So whenever I hear about startups looking to innovate on the decades-old communication tech, I’m instantly intrigued considering the huge number of potential areas of improvement. Plus, talk about a large TAM!
Startups have taken note. Boomerang launched its email productivity software in 2010, and since its 2014 launch, Superhuman has raised $108 million to help users get through their inbox faster. Trying to build a better email mousetrap isn’t exactly a novel concept, but it could be big business.
I recently received pitches from two new upstarts, both of which launched their email innovations in the last year, that really piqued my interest. Let’s meet them.