There is a direct relationship between the complexity of a buyer’s behavior, as they travel through the sales funnel, and the number of online shopping options that currently exist for each product category. In this environment, buyers make decisions in a rather messy and increasingly complex way.
We know, for example, that what happens between the end point of the process (purchase trigger) and the process of deciding to purchase is not linear. Also that there is a complex network of contact points with possible options that vary from person to person. However, what is less clear to us is how buyers process all the investigated information and the criteria that ultimately validate the final purchase.
With the Covid and the associated confinement, there was, suddenly, an increase in the public that moved their purchases from the physical store to the virtual store.
The consumer who was not a regular online shopper realized how practical it is to consume in a virtual shopping center, open 24 hours a day, with the possibility of changing the instant tide store, making specific searches using a large number of criteria to immediately show all possible options and variations.
Every online store that sells an item is one click away, whether it’s a department store or a small boutique, and they all compete for the potential buyer’s attention. To introduce more complexity to the process, if the buyer still cannot decide, they can ask an expert or a celebrity for advice, all are online and permanently available. Additionally, there are millions and millions of anonymous people on the Internet who happily share their shopping experiences, so that you can use it as a background and validate the seriousness and efficiency of possible suppliers.
But … how do consumers today decide what they want to buy and who they want to buy it?
Not surprisingly, companies are interested in knowing the answer to this question for sure. But it is one of the most difficult questions to answer.
A Google market research team, over the course of the past two years, has embarked on a multi-pronged project with the goal of trying to understand
how consumers On the Internet they interpret and manage the information, increasingly, when buying online and offline. This research has led them to identify a specific territory within the maze of searches, ads, links and clicks involved in making a purchase. They called it the “ messy medium ,” a space of abundant information and limitless options that shoppers have learned to manage using a variety of cognitive shortcuts.
Once they defined this territory, they set out to map it. In doing so, they built an up-to-date model of how people behave in this sphere of information abundance and uncertainty. Then with the help of behavioral science experts they recruited people to capture their behavior and listened to them in real time as they told us what they were thinking and doing, and why they were doing it. As they monitored it, we began to notice how seamlessly consumers switch between complementary “scan” and “test” states. They then applied behavioral science to overcome the participants’ explanations and subsequent rationalization to understand the underlying cognitive processes.
Consumer access to media and information has led to the growth of important influences that do not fit within the traditional approach to brand marketing. This has great implications for brands large and small, since it is possible that because they do not understand the change that is taking place in consumption patterns, their marketing initiatives do not have the ROI that they could potentially have.
Of course, figuring out what consumers think and how they behave is not a new Idea. It is an aspiration that has always been at the heart of marketing. But, as we are about to discover, the context in which marketers try to achieve this goal has changed dramatically.
When consumers search the Internet, they often supplement the query with one or more adjectives or other descriptors. They not only search for a product or service, they also search for the one best suited for their specific situation by using additional words. We call these additional words modifiers , and they describe what the user wants to know specifically about what they are looking for. Modifiers provide an emotional snapshot, allowing researchers to see how our feelings and needs have evolved.
In the case of the study that we report in this article, the researchers used the Google Trends tool to study search trends in a specific market and see how these trends vary over time. This very useful Google tool is the same one that we share with our clients every month in the following articles:
By analyzing search trends with the tool, we immediately begin to find some clues that give us useful information. For example, taking the terms “cheap” and “best” in a specific market, in the UK. Interest in search queries containing the word “cheap” has steadily decreased over the last 15 years, while interest in “the best” has consistently increased, as can be seen in the following figure:
These data suggest that sometime around 2009, UK consumers’ interest in finding the cheapest item online was overshadowed by the desire to find the best one. One hypothesis to explain this could be that as median income increases over time, an appetite for wealth signifiers, such as having the “best,” could increase as well. However, when these two trends intersected in 2009, UK households were mired in the economic crisis of 2009 and average household income actually fell. Looking more closely at “cheap” and “better”, it quickly becomes apparent that these two modifiers are very different in scope and application. “Cheap” is quantifiable and rational, “the best” is more subjective and emotional. The value of “cheap” can vary between individuals, but it still has a singular value. “Best”, on the other hand, can have a wide range of meanings, being applicable to value, quality, performance, popularity, and so on.
It is this transition from simple to complex modifiers that offers the first significant clue to how consumer behavior and decision making have changed. As the Internet has grown, it has transformed from a tool for comparing prices to a tool for comparing all kinds of aspects of a product or service.
The Google team’s method was to observe several hundred hours of purchases in 310,000 user purchasing processes investigated in 31 product categories. In the study, each buyer was asked to research a product for which there was buying interest. Trips were recorded using video and audio screenshots, while shoppers explained what they were doing. The shopping trips were then analyzed through the lens of behavioral science by scoring the video playback with the different cognitive biases the researchers observed.
In a post-it note, the researchers drew the purchase trigger at the top and the purchase at the bottom, and in the middle they drew the process they observed in the subjects studied (below).
Very messy … right? This sequence of searching for products and then weighing options amounts to two different modes of mind: exploration and evaluation.
Exploration is an expansive activity, while evaluation is inherently reductive. When browsing, we add brands, products, and category information to Mind Wallets or “Consideration Sets”. By evaluating, we narrow down those options.
Research by the Google team suggests that these two mindsets are cognitively distinct, with different reward systems, and as such require different tactics to connect with consumers depending on whether they are exploring or evaluating.
The difference between giving a consumer information about a category or product and actively closing a sale is subtle but important. In any transaction, the choice is power, and consumers are now more powerful than ever.
Sending the wrong signal at the wrong time could be very damaging to a brand, with the result that the buyer will discard it from the product pool under consideration.
In the model of buying behavior proposed by Google researchers, between the poles of the model are the trigger and the purchase, and in the middle is the disorderly purchase process, in which consumers move between explore and evaluate the options available to them until they are ready to make a final decision. After the purchase comes the experience with both the brand and the product, all of which is fed back into the total amount of exposure of a product or service.
Below we will describe in detail the elements described by the Google research team in their proposed model for the purchase process (image above).
Describing the effect of brand advertising (branding) in a marketing model is difficult. Brands can inspire powerful emotional responses and their impact can be felt throughout decision making in the buying process. Also, the power of a brand is not only derived from advertising. Brands can have a presence beyond marketing: our partnerships with them can be lifelong in some cases, and from a newspaper article to a conversation overheard on the street, it can influence our perceptions.
In the graph, exposure is the sum total of all the advertising and information emanating from a brand in a category. It is the things you have learned through word of mouth, what you have read in the press and in online information. It can be passively assimilated before a purchase trigger, part of the trigger itself, and can be a deciding factor in the final purchase.
But more importantly, the exhibition is not a stage, not a phase, not a step. It is an ever-active backdrop in permanent presence (if the brand is doing well) throughout the duration of a consumer’s purchasing decision-making process.
The model proposed by the Google research team in the form of a loop is the defining characteristic of the messy buying process. Consumers explore their options and expand their knowledge and sets of considerations, then, either sequentially or simultaneously, evaluate options and narrow down their options by discarding.
For certain categories of products or services, only a short time may be necessary to move between these modes. For other more complex purchase categories, the buyer may be encouraged or even forced to participate in a prolonged exploration, generating a good number of options to evaluate.
Finally the buyer to “feel” the impulse to buy can stop the loop completely to go to the purchase stage.
The loop is the best attempt to describe the non-linear nature of the cluttered shopping environment, with its back and forth between destination sites and mind modes
until one lucky brand wins the day. For marketers, the challenge is simple:
How do you ensure that when the buyer stops looping between purchase states (loop) it is your product or service that wins? In other words, how do you persuade a consumer to stop looking and buy what you are selling?
The goal is not to hinder the client or force them out of the loop of exploratory activity they have chosen to undertake, the focus should be on providing everything they need to feel comfortable making a decision.
This last component of the buying model occurs outside of the messy middle loop, once the purchase is made.
The experience a customer has with the product or service they purchased directly increases their exposure in the background. A brand that provides a good experience has an advantage here, and a brand that offers an incredible experience could dramatically increase the frequency of purchases.
However, with so many options available in the market, a brand that offers a bad experience will likely have to work very hard to do business again with a disappointed customer. If it’s a complete mess, your dissatisfaction could be exposed on social media and detectable to other potential customers in the form of negative reviews or comments. (Yes, social media make consumers powerful, not just brands).
In the next stage of research, the Google team tries to test the impact that various behavioral biases can have on shoppers’ brand preferences.
Most scientists studying human behavior today now agree that, in reality, our decision-making apparatus understands both reason and emotion.
In the context of purchasing decisions, we might be tempted to propose that the degree of rationality increases with the size and importance of the purchase. But we all know that buying a car, a house or an expensive vacation and closing the deal still at that time can be plagued with complex emotions. And at the other end of the scale, even in purchases of apparent functionality, low-cost purchases, such as buying a shampoo, can be surrounded by emotional or rational considerations, depending on the individual.
And, of course, to further muddy the water between reason and emotion, advertising, and especially branding often seek to cultivate an emotional connection with consumers; in fact, many people will openly describe how they love or hate a particular brand. These associations linked to our sense of ourselves and our aspirations, of what we want to be, are a powerful source of behavior change in themselves.
To design an experiment that looks at how behavior is influenced during the buying stages described above, the Google team appealed to a list of biases that academic behavioral sciences have described very well. Over the course of more than 50 years, the discipline has codified some 300 principles that explain the conscious and unconscious workings of the human mind. Of course, not all of the 300 are relevant to the type of decision-making that was explored in the study, so during a comprehensive review, the team narrowed the list down to six biases that are closely associated with each other. with the exploration and evaluation phases of the model proposed by Google.
They are shortcuts or rules of thumb that help us make a quick and satisfactory decision within a given category.
An example would be focusing on how many megapixels (MP) the camera has when buying a smartphone or how many gigabytes (GB) of data are included in a phone phone contract.
Princeton, Shah, and Oppenheimer psychologists found that heuristics reduce cognitive effort through the following impacts on decision-making:
Describes the tendency to alter our opinions or behaviors to match those of someone we consider an authority on an issue.
When we are unsure, we tend to follow the lead of people we believe to be credible and knowledgeable experts, and therefore can use an authority view as a mental shortcut.
In one experiment, the brains of 24 college students were scanned while making financial decisions. The students received advice from a renowned economist, the scans showed that the decision-making parts of the students’ brains showed less activity as the students “offloaded” the burden of the decision process to the expert.
Here we see the “why” of the importance of “influencers” in the composition equation of multichannel marketing techniques.
Social proof is a proposal by the psychologist Robert Cialdini that describes the tendency
to copy the behavior and actions of other people in situations of ambiguity or uncertainty.
The internet has digitized word-of-mouth reviews and recommendations in the digital environment, making it much easier for people to get and later rely on social proof as a shortcut to decision-making. Sometimes we are aware of this, for example if we take the time to read consumer opinions in relation to a product or service, but we are often unconsciously influenced. For example, without thinking, we might click on an ad that includes a four or five star rating, lured in by what “appears” to be a popular choice with consumers.
The power of now describes the fact that we naturally tend to want things now rather than “later.” Human beings are programmed to live in the present: our evolutionary survival depended on our ability to deal with the problems of the here and now rather than planning for the future. This explains why people often find it challenging to save for their future. “The Power of Now” also explains the success of instant downloads or 24-hour delivery versus having to wait for a product in a long time.
It is based on the economic principle that scarce or limited resources are more desirable. As Robert Cialdini states: “The scarcity principle is based on our weakness for shortcuts.” Scarcity usually takes one of three forms:
Describe the fact that there is something special about the price of zero.
The demand for a product or service is significantly higher at a price of exactly zero compared to a price even slightly above zero.
In his book “Predictably Irrational,” behavioral economist Dan Ariely writes about a study in which people were given the option to choose between two offerings. One was a free $ 10 Amazon gift card, the other was a $ 20 gift card that can be purchased for just $ 7. More people chose the $ 10 gift card, despite the other option which offers a higher profit value.
The power of free can be thought of as a button that heats up the emotional – a source of irrational arousal that can be instrumental in persuading a consumer to make a purchase decision.
We are clear that this list of 6 cognitive biases is not a definitive list of all the biases in play, we believe that those chosen by the Google team have great referential value, since they represent several of the most powerful principles identified in the scientific literature, all of which are suitable for scale testing. In addition, they also have the advantage that they can be implemented by brands, covering implementations that range from simple changes to a text in campaigns to slightly more complex marketing and logistics decisions, but totally feasible in implementation.
To test whether the stable impact of brand preference and cognitive bias persists across all categories, the Google team selected for an experiment 31 products representing a wide range of risk, complexity, and emotional and financial investment, which covers several major vertical categories and sectors, including travel, financial services, consumer packaged goods, retail, and utilities.
In relation to the participants in the experiment, they were selected with the following criteria:
To ensure a solid sample size for each product, the Google team recruited 1,000 buyers across all categories. This equaled several thousand buyers per sector, and a total sample of 31,000 buyers. Participation was remote, with each buyer completing 10 purchase simulations within a given category, giving a total of 310,000 scanned purchase scenarios within which to analyze the six biases described above.
In the first analysis of the simulation data, the Google team compared the first and second brand preference options, with all other expressions of statistically controlled biases for study neutrality.
For example: A car purchase was simulated (specifically for an SUV). A decision in which several considerations are at stake, such as safety, reliability, efficiency and performance that could reasonably interfere with the study.
In this graph we can see that when a second favorite brand was introduced as an option, 30% of buyers moved away from their first preference.
Simply giving the buyer the option to choose their second brand option was enough to drive 30% away from their initial choice. The car category is full of recognizable brands, so this result may in part be due to two sets of powerful partnerships that battle it out in the buyer’s mind. But what if we look at another category, no less heated? Buying a car is at one end of the spectrum of purchasing complexity in the product matrix proposed by the Google team, so let’s look at a related but less complex purchase: Auto insurance.
According to the proposed product matrix, buying car insurance is not only less complicated than buying a car, it is also less enjoyable (less exciting). These characteristics could partly explain the greater impact of the introduction of a second brand of choice, since it suggests that the purchase requires lower levels of affective participation from the buyer and is therefore more prone to change.
Below is a graph showing all the products in the experiment ordered according to the size of the impact on the preference share when buyers were offered the option of a second brand (the light yellow part shows the share seized by the second favorite brand when displayed next to the first).
Since each brand within a category will have a different level of resilience when presented alongside another brand, the graph cannot be used to predict the extent to which any individual brand will be susceptible to transfer of preference to a competitor.
Favorite brands of consumer packaged goods were, in general, less susceptible to the presence of another brand in the simulations than utility brands such as mobile network, broadband, and energy provider. General retail products such as children’s toys, laptops, TV, clothing and sofas are scattered at all times, while financial services products (mortgages, credit cards, ISA, car insurance) are generally located to the right side, with greater susceptibility to changing preferences.
Having established a baseline to change preference without variation in any of the cognitive biases, the study focused on seeing what degree of change of preference could be achieved by applying the principles of behavioral science.
In almost all cases, social proof (expressed as typical product user ratings, three stars versus five stars) was shown to be the most powerful behavioral bias, be it the largest effect or the second largest effect. in 28 of the 31 categories that were tested in the experiments.
So this is set up in advance and then we’ll look at some of the more nuanced category-specific examples from the study.
Giving people the evidence that other buyers have already had a positive impact on their consumption of a product or service is extremely persuasive. It is the gold standard of social proof – reviews and comments – as it can be difficult for marketers out of nowhere to create fictitious ratings, they are real customers sharing their consumer experiences of the product or service after purchase.
Category heuristics are powerful and relatively easy to implement. In the study simulation, the largest or second largest effect was achieved in 14 of 31 categories. In the scientific literature, category heuristics are defined as shortcuts or rules of thumb that help people make decisions: vital pieces of information that help clarify our options, such as the amount of memory in a computer portable or the amount of carats in a diamond.
To make effective use of the category heuristics, marketers must understand which characteristics consumers most associate with a product or service. Often times, this is also the feature that they value the most. For example, when we looked at broadband, we found that highlighting data allocations achieved the largest share of preference transfer, outside of the brand that was initially favored.
The category heuristic also turned out to be a decisive factor in the financial vertical,
for example mortgages or car insurance. In these highly structured products, the study simulations show that consumers are particularly prepared to look for value characteristics such as the duration of a fixed rate:
Or the validity periods in the treatment of claims for insurance:
Although the study’s simulation shows it to be less powerful than its close cousin, social proof, authority bias is still a very effective way to reassure buyers by citing awards and expert reviews. This was particularly effective in categories where consumers may feel disadvantaged due to a lack of domain-specific knowledge, such as home furnishings and electronics. Unsurprisingly, the simulation also found that when it comes to authority, endorsement from a well-known and unbiased publication tended to carry more weight.
Shortage messages are perhaps one of the most recognizable. However, in the simulations of the study it was most of the time the least effective bias. While it can be effective as a deciding factor during the “final evaluation” (last stage of purchase), for the “explore” stage feeling restrictive causes a negative reaction in consumers.
The simulation findings of the study show that the power of the “free” can have a great influence on behavior, with either the greatest or the second greatest effect on the transfer of preferences in 18 of 31 categories. .
In the car rental category, we tested the power of free by boosting the buyer’s favorite brand with a free final car cleaning, while the second favorite brand offered an additional day of free rental. This effect turned out to be the third most powerful of all the biases tested in the study, with a carryover of 70%.
While gift giving worked well with expensive transactions, the power of free has also proven effective with cheaper purchases. A buy one get one free offer (BOGOF) was the second most effective expression of a bias in the transfer of brand preference in the detergent category, while free popcorn at the movies also achieved a second place .
In the short haul category, we see an interesting example of how biases are sometimes combined, with a free checked baggage. Below is the graphic of this example.
The immediate gratification of express delivery didn’t make much of a difference in the simulation, but it still had a significant effect in a handful of categories. In fast-moving consumer goods (FMCG), products like detergent, moisturizer, cereals, and cat food, all saw consumers respond positively to overnight delivery offers. Same day delivery also had a noticeable effect on the children’s clothing and toys categories.
Finally, to explore the more extreme implications of the study’s findings, the Google team introduced a full wildcard. They created a dummy test brand
to assess how much shared preference an unknown challenger might take.
Even with everything learned in the study on the power of behavioral principles, the results with the fictitious competitor were a big surprise. For example, in the mobile phone network category, the fictitious brand, Gem Mobile, was able to “steal”
almost a 50% preference from the favorite brand.
Following the lead of Gem Mobile, the researchers also created Intergo, a new fictitious broadband provider to test against established competition. Similarly he threw all the advantages behind this newcomer, and the effect turned out to be even more eye-catching. In this case, Intergo was able to claim 73% of the brand preference.
Before we get too emotionally driven, we should note that to achieve these meaningful preference results, the two challenging brands created by the researchers needed to spice up their new brands heavily with value propositions superior to the leading brand. Value propositions are likely out of reach even for a well-funded challenger. This is particularly true of the volume of positive reviews needed to build persuasive social proof, which must be earned over time as consumers experience a product or service.
Additionally, the behavior is not the same in all categories, as we can see in the following graph:
Entering new markets is a challenge. Even if brands skip typical operational barriers to entry. Marketing budgets and brand partnerships are considerable and present yet another obstacle to conquering new markets. The agile and intelligent use of the science of human behavior could give brands a vital advantage and a big difference between failure or success.
In a future article we will follow the analysis of this interesting Google study and we will continue with our conclusions and reflections … there is a lot of fabric to cut.