The Consumer Insights Decision Making Dilemma: How to Integrate Conscious & Implicit Association Research Data
By Alena Jule
August 14, 2014
Many insights professionals have started to think of the conscious mind as the tip of the consumer thought iceberg, with the vast majority of the mass lying beneath the surface.
Furthermore, traditional research approaches have been positioned as only tapping into the conscious bit above the surface, thereby drawing into question whether we should really be using these conscious measures at all, let alone as our primary source for consumer behavior insights.
This debate on the value of conscious versus subconscious, or implicit association, research has been gaining strength in the past five years.
In this video from IIeX, Dr. Aaron Reid turns that debate on its head by making a strong case for the necessity of both conscious and subconscious research methods in any consumer insights practice.
Beyond arguing that both are necessary for a holistic view of the consumer, Dr. Reid argues that we need to use the best conscious measures that have been developed over the past 30 years in market research and not merely rely on stated likelihood-to-purchase questions or explicit rating scales.
Integrating subconscious measures with our best derived conscious assessment tools offers unprecedented predictive validity in our models of consumer behavior. Five case studies forecasting sales data from fashion to oatmeal show the predictive advantage of this integration.
[fve]http://youtu.be/-7RGUv3eRK8[/fve]
TRANSCRIPT:
>>Dr. Reid: So, if we’re tapping System 1 in that way, how do we tap System 2? We need to make sure we’re not throwing out our best methods, that we’ve developed over the last 30 years.
System 1, as we’ve talked about, is associative. System 2 is deliberative. So when we do surveys, we’re asking people to deliberate. Deliberate on this and give me an answer.
But, that’s not the only way for us to get at deliberative processing. In fact, we have much more advanced ways to do it. And let’s put this in a product case study perspective. So let’s say, you’re trying to understand how well this product is going to do on the market. This is an actual case study, and I’m going to show you the sales results.
This product is coming on the market. It’s a shirt. It’s made by Calvin Klein and it’s offered for $49.99. One way you might evaluate this is to ask a likelihood-to-purchase question.
Now, probably at this conference I’m safe in saying, “gosh, we knew that these questions were not predictive a long time ago.” (You can’t always say that at every market research conference.)
But when we think about combining conscious and subconscious methods, let’s use the best conscious methods that we have. When you’re bringing a new product to market, and you’re trying to understand the influence of brand versus product versus price, you want some derived trade-offs.
It’s a deliberate choice, so you’re accessing System 2, but it’s derived data of that deliberate choice. It’s much better data.
So, here’s a choice-based conjoint study. We’ve got different products. We’ve got different brands. We have different prices. We can mix and match. We can isolate the influence of the product versus the brand versus the price on choice. It’s great data! We use this all the time to try to forecast sales.
That’s what we want to use. We get expected utility formulas, which are wonderful, to some degree. But we know that expected utility is lacking something in its predictive utility.
So let’s show you the sales results. We ran this study. A great client of ours, Macy’s, shared this case study with us and allowed us to share the data, so thank you for that.
They were coming to market with a new line of products for a brand, and they wanted to understand which products were going to be most successful in the market place. And we said “OK, let’s do this. Let’s measure a conjoint, so we have the rational, the conscious, and let’s do our implicit associations, the subconscious, so we have the System 1 processing as well.”
And by the way, we’re going to do this before you go to market with a product. So we made the predictions in April. They went to market with the product in May. So the product had already been bought. It had already been stocked. They knew which product was going to be stocked, and we said “can you give us the buy on each product, which is how much they spent on each product that was gong to market,” which they did.
And so we compared the “buy” to the actual sales. As you can see there, the buyer predictions are on the X axis, and the sale of each product is along the Y. The r is a .53. You might think that that’s great! A .53 correlation. But you might think that it’s not that great when you think about an r-square value, which is the amount of variance that’s accounted for: 28%! So gosh, let’s at least do some research.
Well here are the results from the conjoint. The conscious model accounts for 69% of sales. That’s fantastic. But we know that that’s only System 2 processing.
What happens when we take the implicit and we combine it with the explicit? The r-square goes up to 94%! We’re accounting for 94% of actual in-market sales when we combine these two methods.
So, here’s fashion study number two. Was that just a fluke? it was only a few products.
We did it on a different line of products. Here the r is .93. That’s combined conscious and subconscious data.
Here’s fashion study number three. Combined subconscious and conscious data. The r is .92. And just to show you that it’s not always .9, here’s conscious and subconscious study number four, with an r of .89.
So when we present that, people ask us, “well, fashion is very emotional. Does this work for other categories?”
So we said, “what about oatmeal? How about hot cereal?” Is emotion relevant in that decision? That might evoke some feelings of disgust, actually. So, yes.
But when you involve the brand, it’s probably much more emotional. All of those brands are imbued with emotional value.
So we did a deliberative, choice-based conjoint study. It was a price/pack-size study. And we also measured emotional associations. And in this case, we got sales data. We did the study in quarter four, and we got sales data from Q1, so it’s all forward looking.
Sales data is on the Y, predictions are on the X. This is the conscious model: an r of .64, accounting for 41% of sales. That’s the conjoint.
Here’s the emotional model. An r of .71, accounting for 51% of sales. That’s just the implicit data. So, if you had to choose just one, you’d say “OK, give me the implicit.”
But the point is, you don’t have to choose one. And we shouldn’t choose one. We should combine our best conscious and subconscious methods together.
And when you combine them together, we have an r of .9, predicting 80% of actual market sales for in-market products, before they even occurred.
May I go one more? The crystal ball. The last point that I want to make here against different methods is that, if this represents all sales, and we know that this is conscious preference and this is subconscious implicit, when we combine them, they’re much more accurate together.
Furthermore, traditional research approaches have been positioned as only tapping into the conscious bit above the surface, thereby drawing into question whether we should really be using these conscious measures at all, let alone as our primary source for consumer behavior insights.
This debate on the value of conscious versus subconscious, or implicit association, research has been gaining strength in the past five years.
In this video from IIeX, Dr. Aaron Reid turns that debate on its head by making a strong case for the necessity of both conscious and subconscious research methods in any consumer insights practice.
Beyond arguing that both are necessary for a holistic view of the consumer, Dr. Reid argues that we need to use the best conscious measures that have been developed over the past 30 years in market research and not merely rely on stated likelihood-to-purchase questions or explicit rating scales.
Integrating subconscious measures with our best derived conscious assessment tools offers unprecedented predictive validity in our models of consumer behavior. Five case studies forecasting sales data from fashion to oatmeal show the predictive advantage of this integration.
[fve]http://youtu.be/-7RGUv3eRK8[/fve]
TRANSCRIPT:
>>Dr. Reid: So, if we’re tapping System 1 in that way, how do we tap System 2? We need to make sure we’re not throwing out our best methods, that we’ve developed over the last 30 years.
System 1, as we’ve talked about, is associative. System 2 is deliberative. So when we do surveys, we’re asking people to deliberate. Deliberate on this and give me an answer.
But, that’s not the only way for us to get at deliberative processing. In fact, we have much more advanced ways to do it. And let’s put this in a product case study perspective. So let’s say, you’re trying to understand how well this product is going to do on the market. This is an actual case study, and I’m going to show you the sales results.
This product is coming on the market. It’s a shirt. It’s made by Calvin Klein and it’s offered for $49.99. One way you might evaluate this is to ask a likelihood-to-purchase question.
Now, probably at this conference I’m safe in saying, “gosh, we knew that these questions were not predictive a long time ago.” (You can’t always say that at every market research conference.)
But when we think about combining conscious and subconscious methods, let’s use the best conscious methods that we have. When you’re bringing a new product to market, and you’re trying to understand the influence of brand versus product versus price, you want some derived trade-offs.
It’s a deliberate choice, so you’re accessing System 2, but it’s derived data of that deliberate choice. It’s much better data.
So, here’s a choice-based conjoint study. We’ve got different products. We’ve got different brands. We have different prices. We can mix and match. We can isolate the influence of the product versus the brand versus the price on choice. It’s great data! We use this all the time to try to forecast sales.
That’s what we want to use. We get expected utility formulas, which are wonderful, to some degree. But we know that expected utility is lacking something in its predictive utility.
So let’s show you the sales results. We ran this study. A great client of ours, Macy’s, shared this case study with us and allowed us to share the data, so thank you for that.
They were coming to market with a new line of products for a brand, and they wanted to understand which products were going to be most successful in the market place. And we said “OK, let’s do this. Let’s measure a conjoint, so we have the rational, the conscious, and let’s do our implicit associations, the subconscious, so we have the System 1 processing as well.”
And by the way, we’re going to do this before you go to market with a product. So we made the predictions in April. They went to market with the product in May. So the product had already been bought. It had already been stocked. They knew which product was going to be stocked, and we said “can you give us the buy on each product, which is how much they spent on each product that was gong to market,” which they did.
And so we compared the “buy” to the actual sales. As you can see there, the buyer predictions are on the X axis, and the sale of each product is along the Y. The r is a .53. You might think that that’s great! A .53 correlation. But you might think that it’s not that great when you think about an r-square value, which is the amount of variance that’s accounted for: 28%! So gosh, let’s at least do some research.
Well here are the results from the conjoint. The conscious model accounts for 69% of sales. That’s fantastic. But we know that that’s only System 2 processing.
What happens when we take the implicit and we combine it with the explicit? The r-square goes up to 94%! We’re accounting for 94% of actual in-market sales when we combine these two methods.
So, here’s fashion study number two. Was that just a fluke? it was only a few products.
We did it on a different line of products. Here the r is .93. That’s combined conscious and subconscious data.
Here’s fashion study number three. Combined subconscious and conscious data. The r is .92. And just to show you that it’s not always .9, here’s conscious and subconscious study number four, with an r of .89.
So when we present that, people ask us, “well, fashion is very emotional. Does this work for other categories?”
So we said, “what about oatmeal? How about hot cereal?” Is emotion relevant in that decision? That might evoke some feelings of disgust, actually. So, yes.
But when you involve the brand, it’s probably much more emotional. All of those brands are imbued with emotional value.
So we did a deliberative, choice-based conjoint study. It was a price/pack-size study. And we also measured emotional associations. And in this case, we got sales data. We did the study in quarter four, and we got sales data from Q1, so it’s all forward looking.
Sales data is on the Y, predictions are on the X. This is the conscious model: an r of .64, accounting for 41% of sales. That’s the conjoint.
Here’s the emotional model. An r of .71, accounting for 51% of sales. That’s just the implicit data. So, if you had to choose just one, you’d say “OK, give me the implicit.”
But the point is, you don’t have to choose one. And we shouldn’t choose one. We should combine our best conscious and subconscious methods together.
And when you combine them together, we have an r of .9, predicting 80% of actual market sales for in-market products, before they even occurred.
May I go one more? The crystal ball. The last point that I want to make here against different methods is that, if this represents all sales, and we know that this is conscious preference and this is subconscious implicit, when we combine them, they’re much more accurate together.
