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Journal of Marketing at Retail 22 Published by POPAI © 2012. All Rights Reserved.

The Four Types of Customers: New Lessons in Shopper Segmentation & Hierarchy of Needs

ABSTRACT This paper proposes that the methodology utilized by POPAI for the Shopper Engagement Study provides a sound framework for developing a universally applicable shopper segmentation model. As the project is comprised of two concurrently executed modules it provides greater insight and datasets for which to observe and further understand shopping behavior.

AUTHORED BY: Gregory SmithDirector, CommunicationsPoint of Purchase Advertising International

Today’s retail landscape is undergoing massive shifts. The external environment is undergoing profound changes. The rise of new media alternatives, especially user-generated media, such as social networks and product review sites, has provided shoppers with unprecedented influence over the quantity and quality of content they access. The shopper’s ability to exert control over communication channels has changed the interface between organizations and the market. Shifting power relations have major implications for shopper expectations, purchasing decision-making, distribution, concepts of customer value and the way that business is transacted.

As such, it’s arguably more important than ever to know and understand the shopper and what is driving his/her behavior. There, of course, are a number of ways to quantify the shopper. Racial, economical, and geographical demographics have ruled the day in defining a target audience, but are these data points the best or most telling way to understand the shopper? Certainly they have a place in trying to better understand the shopper, but an emerging way of profiling shopper behavior is arising and becoming more useful for shopper marketers - shopper segmentation.

Increasing market fragmentation, heterogeneity of demand and the rise of knowledgeable, sophisticated shoppers who want to be treated individually has been well documented

(Firat and Shultz, 1997; Hart, 1995; Proctor and Kitchen, 2002). Retailers and brand manufacturers alike have come to realize that using shopper data to its greatest advantage starts by having a shopper marketing strategy that will create value for their respective customers. Studies have demonstrated that it is more profitable to grow the bottom line by catering to existing shoppers than it is to obtain new shoppers (e.g., Hauser et al. 1994, Griffin et al. 1995, Ittnery and Larcker 1998, Morgan et al. 2005). Yet many retailers and brand manufacturers spend a considerable portion of their time, energy, and resources chasing new business.

Although it is important to replace lost business, grow the business and expand into new markets, one of the primary goals should be to keep existing customers and enhance customer relationships. Conventional wisdom suggests that it costs at least five times more to get a new customer than to keep an existing one (Weinstein, 2002). According to Weinstein, in many markets, share of customer, which is a customer retention measure, has supplanted market share, which is a customer attraction measure, as the relevant business performance objective. Consequently, a good understanding of customers’ purchasing patterns helps companies keep customers and gain a greater share of their business. Knowing who the most important shoppers are and segmenting shopper data so that retailers and brand manufacturers can manage those valuable segments is

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essential in today’s highly competitive retail landscape.

MARKET SEGMENTATION IN REVIEWBrand marketers pioneered market segmentation in the mid twentieth century in response to the availability of data. Demographic and purchasing data was available for groups but rarely for individuals. Similarly, advertising and distribution channels were available for groups, but rarely for single consumers.

Wind (1978) identifies four basic approaches to segmentation; two traditional methods (a-priori and post hoc) and two flexible methods (dynamic and componential). Since this typology was proposed, a more recent class of techniques has been developed. During the 1990s, researchers developed methods that combine competitive market structure (CMS) with segmentation methods. These methods have been frequently cited in the literature and only a brief outline will be provided here (Allenby, et al., 2002; Tynan and Drayton, 1987).

Traditional a-priori and post hoc methods differ with respect to the selection of an appropriate base. A-prior methods require the analyst to select a base for segmentation prior to analysis while post-hoc methods arise from a base for segmentation after analysis (Hoek, et al., 1998; Tynan and Drayton, 1987; Wind, 1978).

A-priori segmentation classifies people into groups based on variables that are considered most relevant. For shopper marketers, such variables may include age, gender, and household income. The a-priori approach provides broad behavioral insights about a population, but it rarely explains why people make particular decisions or engage in specific behaviors.

Post hoc segmentation is typically based on primary research regarding people’s activities, interests, and beliefs. Segments emerge from this approach because of similarities in participants’ responses across multiple variables rather than a-priori intuition for specific, pre-

determined variables. Because the post hoc method is based on underlying motivations, it provides a very rich description for explaining the behaviors and developing more powerful marketing programs.

A wide range of techniques are then available to develop shopper segments including cluster analysis, factor analysis and discriminant analysis although most commercial segmentation studies rely on cluster analysis (Hoek, Gendall and Esslemont, 1998).

Dynamic segmentation, a flexible approach, analyzes consumer responses to the attributes of test products and typically relies on some type of choice modeling in simulated conditions (Roberts, 2000; Tynan and Drayton, 1987; Wind, 1978). Componential segmentation, an extension of dynamic, shifts the emphasis away from partitioning and onto prediction (Green, 1979; Moore, 1980). Multidimensional scaling or hierarchical clustering techniques are favored by these predictive approaches.

Finally, a number of new approaches combining CMS with segmentation have been developed. Frequently involving both explanatory and predictive components (Elrod, et al., 2002; Reutterer and Natter, 2000; Russell, et al., 1999), these methods variously employ self-organizing maps, fuzzy clustering, typology representing networks and latent class techniques to reveal inherent market structures and segments (Reutterer and Natter, 2000).

The primary purpose of segmentation is to identify segments that differ in their purchasing power, aspirations and market behavior (Allenby, et al., 2002; Hoek, et al., 1998; Yankelovich and Meer, 2006). Most segmentation studies rely on one-off data collection, in which respondents’ self reported statements form the core data set (Hoek, Gendall and Esslemont, 1998; Wind, 1978). Typical data inputs consist of purchasing, consumption or attitudes towards the brand suggesting that the brand remains the primary unit of analysis (Dibb, 2002; Hammond, et al., 1996). As such, most segmentation studies address immediate short-term

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questions; typically the “market served” and are used to inform operational marketing decisions. In short, traditional segmentation has been a tactical, brand driven process.

Instead of looking at the brand level for segmentation, however, it may be more prudent to look at shopper behavior modes at a macro-level, which would be shaped by the retail channel, trip type, and shopping occasion. This model would provide a true benchmark for retailers and brand marketers in determining how well they are retaining and attracting their target shopper segments internally as well as where they are losing out to the competition.

A UNIVERSAL FRAMEWORK FOR SHOPPER SEGMENTATION Even with the proliferation of research and literature on the subject of shopper market segmentation very little has been done in field to develop a model that correlates the attitudinal findings of shoppers’ with their emotional and physiological responses to in-store stimuli to define how different shoppers behave and react to the retail environment.

To this end Point of Purchase Advertising International (POPAI), the global association for the marketing at retail industry, fielded a major study in August 2011 to understand shoppers’ attitudes, behaviors, and emotional responses in the supermarket class of trade to specific types of displays, during shopping trips. Beginning in 1965, POPAI has undertaken a number of shopper research projects aimed at providing new information on how shoppers behave when they are in different types of stores deciding which products to buy. Since the primary outlets for many packaged goods are supermarkets, POPAI has conducted a series of five studies of supermarket shoppers. Those studies were done in 1965, 1977, 1986, 1995, and earlier this year – 2012.

The most recent study utilized many of the same methodologies that POPAI has employed in their extensive series of periodic shopper purchasing behavior studies, but has capitalized upon developments in technology to gain deeper insight into the shopper. The series of studies

have traditionally relied upon a Pre/Post Shopper Intercept Interview Model. The latest iteration, however, builds upon that model by incorporating portable electroencephalography (EEG) and eye-tracking equipment worn by respondents while they shop. The study module, described in further detail in the following pages, allows POPAI to tie shoppers’ physiological responses to exactly what they were looking at during their shopping trip helping to mitigate issues such as recall and denial that is a source of contention with the interview model.

This paper proposes that the methodology utilized by POPAI for the Shopper Engagement Study provides a sound framework for developing a universally applicable shopper segmentation model. As the project is comprised of two concurrently executed modules it provides greater insight and datasets for which to observe and further understand shopping behavior. The Core Methodology includes pre/post shopping interview format consistent with the execution of POPAI’s Consumer Buying Habits Study. This allows POPAI to calculate key purchase decision metrics that are comparable with those from the earlier studies. The second module of the study, which utilizes eye-tracking/EEG executions with a subset of those shoppers, allows POPAI to examine the neurological responses and effects of in-store stimuli in the shopper’s journey.

DATA COLLECTIONThe Point of Purchase Advertising Institute (POPAI), the global association for the marketing at retail industry, periodically conducts an extensive field study of shopper purchasing behavior. Dating back to 1965 this widely cited study has been used by business managers and academic researchers alike to examine the extent of in-store decision-making by shoppers.

POPAI fielded its most recent study in the fall of 2011 at a cost of approximately $500,000. In-store intercept interviews were conducted with 2400 shoppers across the four broad U.S. census regions. In addition, a subset of 210 shoppers were recruited to participate in the EEG/Eye-

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tracking portion of the study.

CORE METHODOLOGYSAMPLEA total sample of 2400 supermarket shoppers were interviewed for the core methodology. The study was executed across the four (4) broad US geographic census regions across two markets per region. The number of interviews conducted per region closely reflected a census balanced sample.

The study was conducted across thirteen major supermarket banners. These banners were large, high-volume stores selected from leading chains and were evenly divided among the four (4) geographic census regions.

SHOPPER INTERCEPT INTERVIEWSSupermarket shoppers were randomly intercepted and screened at the entrance of each store location for being at least 16 years of age and on a “major shopping trip”. These interviews were conducted across all day parts and all days of the week.

The 10-minute entry interview gathered information on:• Planned purchases (unaided category and brand

planning)• Any pre-store path-to-purchase activities shopper

engaged in for planned purchases and in general• Amount budgeted/expect to spend for planned items

and total basket• General shopping behaviors in channel• Demographics and profiling information

Upon completion of the entry interview, shoppers were asked to return to the researchers after they completed their shopping trip for a 15-20 minute post-shopping interview. A $25 store gift card incentive was provided to each shopper who returned and completed the post-shop interview. Shoppers also agreed to have information on all of their purchased products recorded. The record of all purchases for each shopper was obtained through electronic capture of register receipts.

After completing their shopping trip and checking out the ethnographer then conducted the exit interview and recorded products and brands purchased from shopper’s entire basket.

The exit interview gathered information on:• Products purchased (category and brand level

information)• Coupon, circular, mobile phone use, etc. used in

purchase decisions• Recall/awareness of displays for product purchases• Attitudes and perceptions towards retail environment

and specific categories. Each shopper was probed on at least three high volume categories - e.g. salty snacks, CSD, toys, etc – so that expanded category specific insights could be provided.

• Total amount spent and by category.• Method of payment

STORE AUDITEach day an audit of specific display types throughout the store was conducted prior to the start of interviewing. The purpose of the store audit was to record and identify the display materials for which to measure impact on decision-making and emotional response. The audit included photos of each display logged as well as coding of the following information:

Display Type:• Floorstands• Endcaps• Powerwings/sidekicks• In-line/gondola/full-line merchandisers (specialty)• In-store media• Digital signage

Location of each display:• End of aisle (front or back)• Perimeter/racetrack• In-aisle• Front end

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Placement of display:• Primary• Secondary

Other variables:• Category and brand the display is advertising• Static vs. motion• Product on it vs. no product• Whether it is interactive• Whether it has video or audio

DATA SETSThe data collection resulted in three distinct data sets:• Shopper Database that includes all shopper specific data

such as demographics, general shopping behaviors, shopping trip characteristics, attitudes and perceptions of shopping trip and display recall.

• Purchase Database that includes all purchases made by each shopper at the category and brand level including the type of purchase decision and any use of coupons, mobile, etc.

• Display Database that includes an inventory of all products and brands, by display type and other coded variables.

SECONDARY METHODOLOGYOBJECTIVESAs an overlay to the core methodology, POPAI utilized mobile eye-tracking and EEG on a sub-set of shoppers to understand the following:• Degree to which displays (as defined earlier) make it

into shoppers’ line of sight.• Identify the display types and locations that generate the

most impressions and greatest activation.• Amount/percentage of time spent engaging with these

displays (by audit variables such as display type, location, and placement).

• Degree to which these displays are noticed, stopped at, interacted with and drive purchases.

• What is the emotional response (valence) to these displays?

• What is the shopper’s track or path throughout the store?• For 20 high volume categories what are the search

patterns and navigational strategies shoppers use to find and select products? How much time is spent considering these categories?

SAMPLETraditional primary research methods measure introspective opinions. They rely on techniques such as surveys or facilitated focus groups. These measures require a large sample size in order to ensure valid results.

The individual responses analyzed with the secondary methodology exhibit low variability within a specified demographic. In order for us to achieve the linkage between the core methodology and secondary methodology the sub-set of eye-tracked shoppers came from the pool of in-store interviews conducted in the core methodology. Shoppers were not forced to view any specific displays so as to not create bias.

A sample of 210 study participants evenly spread across the four (4) census regions yielded stable and valid results.

ELECTROENCEPHALOGRAM DATAThe primary research tool utilized in the secondary methodology of the study was high-density Electroencephalogram (EEG) data. When groups of neurons are activated in the brain, a small electrical charge is generated, resulting in an electrical field. EEG is a method that is used to measure these fields by placing electrodes on a person’s scalp. The measured signals are then amplified for analysis.

These electrical fields were interpreted and projected onto a high-resolution, three-dimensional representation of a brain. The result is the ability to determine what areas of the brain are activated at specific moments.

Utilizing a standard mobile EEG framework, the testing procedures followed advanced neuroscience-based acquisition and analysis protocol provided by Sands Research. By running one participant at a time the study

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was able to maintain absolute quality of EEG data to ensure effective and valid results with statistical confidence.

SYNCHRONIZED EYE-TRACKINGThe use of eye-tracking data is common in market research studies. However, adding a real-time interpretation of the brains response to a specific instant in a recording, to interpret why a subject is looking at a target, is not. Correlating EEG and eye-tracking data allows for a time-locked analysis of how shopper interaction plays into the perception of the presented material. By utilizing traditional methods of market research as well as brain response tied to eye-tracking we were able to relate unparalleled accuracy in actual shopper experience.

Via eye-tracking and statistical analysis combined with the EEG recording, we were able to evaluate how much effortful processing is necessary for an individual to interpret what he/she sees. With Sands Research proprietary brain imaging software, we measured activity in the orbital frontal lobe

and displayed those results second by second in real time. Additionally, results of what percentage of the total study population viewed and which part of the design presentation achieved the largest response. This was achieved by marking when the participant fixates on a specific product or component with a unique event code. This allows for analysis to sort and group responses based on that unique event code.

PARAMETERS AND PROCEDURESUtilizing a neuromarketing analysis framework the responses of all 210 participants was directly recorded combining EEG and eye-tracking in a real world environment and in real-time. The in-depth study included 64 channels of EEG data along with eye-tracking. While the eye-tracking data can provide interesting insights, its inclusion with EEG data allows the brain activity to be synchronized with specific objects of interest that the participant is fixating on.

The testing parameters and procedures for the retail EEG/Eye- tracking were as follows:• The sample consisted of 210 participants within the

market segments of interest.• Individuals were recruited to participate in the study.

Participants were staggered at 45 or 60-minute intervals to reduce flow congestion.

• Two acquisition teams were deployed into the field into separate markets simultaneously.

Participants were prepped using the following planned sequence:• Pre-testing • EEG and Eye-tracking prep • Recording EEG & Eye-tracking data across 20+

categories • Removal of equipment • Post questioning sequence • Issued incentive

The entire process took about 90 minutes, on average, to complete. The individuals were paid a participation incentive ranging from $100 - $150.

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SHOPPER SEGMENT CLASSIFICATION As part of the extensive interview, shoppers were asked to rate their level of agreement with a broad set of lifestyle and shopping characteristics. POPAI utilized cluster segmentation in order to isolate unique and distinct shopper groups, each defined by their purchase drivers. By taking a close look at these shopper segments we can explore shopping behaviors, price sensitivities, retailer selection, loyalty drivers, demographics, and decision-making patterns. This national sample based representation of shoppers provides us with a macro level understanding of supermarket retail shopper segments. It allows manufacturers, brands and shopper marketing strategists to overlay what we’ve identified about these actionable segments to what they know about their brands and shoppers. More importantly, retailers and brand manufacturers can utilize and update

the data in their own segmentation research if they collect data from the questions in Appendix 1 and plug it into the algorithm calculation outlined Appendix 2.

ALGORITHM CALCULATIONThe algorithm consists of the following 16 coefficients outlined in Appendix 2, one per segment, plus a constant.

Prediction of segment membership is a three-step process:1. For the first segment, multiply the response scaling by

the coefficients for each question. 2. Add the 16 products together along with the constant to

calculate an overall segment score.3. Repeat for each of the other segments and compare the

segment scores. The algorithm’s predicted segment is the one with the highest score.

I’m going to read some statements about your attitudes toward shopping. Please tell me how much you agree with each of these statements. Please use the scale on this card, ranging from Strongly Disagree to Strongly Agree.

Question # QuestionStrongly Disagree

1 2 3 4

Strongly Agree

5

1 I will go to another store if I can get a better price

2 I tend to stick with the same store regardless of the prices they have

3 I am willing to wait for a product to go on sale before I buy it

4 I don’t mind paying full retail price for products I need

5 The best shopping trip is one where you get exactly what you set out for

6 I am happiest when I get done shopping

7 My primary aim in shopping is to complete the trip as planned

8 You should focus on getting the shopping done rather than looking around at whatever catches your fancy

9 I enjoy getting meal ideas while shopping

10 I enjoy seeing what new products are available

11 I enjoy browsing the store when I grocery shop

12 In my everyday life I feel pressure from not having enough time

13 In my everyday life I feel pressure from not having enough money

14 I feel a lot of time pressure in my life

15 I am always in a hurry

16 I wish I had more free time

Scaling 5 4 3 2 1

APPENDIX 1 Attitudinal Questions Regarding Shopping Trip

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FIGURE 1Shopper Segment Breakout

Question # Segment 1: Time Stressed

Segment 2: Explorer

Segment 3: Trip Planner

Segment 4:Bargain Hunter

Q.1 1.866 1.920 2.001 2.497

Q.2 4.116 4.194 4.252 2.958

Q.3 .172 .449 .319 .870

Q.4 3.002 3.155 3.036 2.059

Q.5 1.858 2.319 2.415 2.047

Q.6 1.253 1.509 1.801 1.138

Q.7 3.265 3.598 3.848 3.258

Q.8 1.286 .665 1.783 1.250

Q.9 .656 1.191 .799 .491

Q.10 1.661 2.058 1.740 1.631

Q.11 2.027 2.465 1.721 2.003

Q.12 .937 .386 .444 .442

Q.13 .406 .282 .165 .266

Q.14 .464 -.084 -.309 -.163

Q.15 .743 -.081 .007 -.011

Q.16 .640 .591 .461 .537

Constant -48.583 -51.342 -51.141 -38.683

APPENDIX 2 Segmentation Algorithm Calculation Tool

29%26%

Explorer Trip Planner Bargain HunterTime Stressed

RESULTING SHOPPER SEGMENTSWhen shoppers walk into a supermarket, they do not just reveal where they like to buy their food but also a whole host of lifestyle values, from what they read and watch to what they like doing in their spare time. But as POPAI’s Shopper Engagement Study proves, today it is increasingly difficult for retailers and brand manufacturers to shoe-horn their customers into one specific group.

Our predictive shopper profiles contain insights on what shoppers like – based on their stated preferences, their browsing habits, and the products that they actually purchase or abandon in their shopping trips.

Shoppers are clustered into segments to understand the attributes and characteristics that are most important to individuals as they decide where to shop. Shoppers were asked to rate their level of agreement with a broad set of lifestyle and shopping characteristics on a five-point scale. Using factor analysis we are able to create shopper groups based on these lifestyle and shopping statements. These factor groupings form the basis of the creation of shopper segments, which can be analyzed for shopping behavior patterns, price sensitivity, retailer preferences, retailer loyalty, demographic differences, and opportunities for conversion.

These segments have unique attitudes and behaviors as they relate to their shopping patterns and retailer selection. So what shopper profiles have emerged with regards to today’s shoppers? The study revealed four basic profiles in shoppers’ paths to purchase across all the supermarkets: Time Stressed, Explorer, Trip-Focused and Bargain Hunter.

Figure 1 shows that each of theses segments

23%22%

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account for approximately the same proportion, with the “Time Stressed” group capturing the largest share at 29%. While these segments are proportionally similar their behaviors are quite varied. The shopper profile of each of these segments is summarized below.

THE TIME STRESSEDThis group can be characterized by their sense of pressure from not having enough time and always seeming to be in a hurry. They spend the least amount of time in active shopping mode (along with Bargain Hunters) and view fewer displays. Compounding their time pressures are perceived budgetary constraints despite that this group is not low income. This younger skewing cohort is likely to be shopping with children and demonstrate a proneness to in-store decisions. They do not typically plan with circulars and coupons, are least consistent with using a written list and describe themselves as easily tempted. The results of these qualities include high impulse baskets and the highest total basket dollar average. Appendix 3 shows the demographic breakout, top impulse categories, and further suggestions on how to engage this shopper segment. THE EXPLORERExplorers enjoy seeing what new products are available, browsing the store and getting inspiration for meals while shopping. They visit more aisles than average and this navigational spread coupled with an exploratory nature result in their viewing the highest number of displays (almost double that of Bargain Hunters). They are similar to the Time Stressed in that they describe themselves as easily tempted and have a high impulse basket as well, yet they rely heavily on circulars to drive retail choice and are particularly receptive to stores with quality private label products. Explorers make the most weekly trips to the store and represent a very valuable shopper as they are the most satisfied segment. They boast the highest level of impulse baskets despite their older skew and lower income profile. Appendix 4 shows the demographic breakout, top impulse categories, and further suggestions on how to engage this shopper segment.

THE TRIP FOCUSEDThe goal of this shopper is to efficiently execute their shopping trip. Not surprisingly, the Trip Focused segment is comprised of more males and is a bit older. They make the fewest trips per week and not only do they spend the most total time shopping, their time spent in the aisles is longer than all of the segments. While they use lists more consistently than other segments (most likely as a way to stay focused), they do not use circulars much. Mission-oriented, they describe themselves as controlled and restrained and are not interested in bargain hunting. Additionally, they are the most loyal to their retailer. They have the lowest percent of impulse baskets and can most accurately predict what they will spend, indicating that they stay close to their plan. Appendix 5 shows the demographic breakout, top impulse categories, and further suggestions on how to engage this shopper segment. THE BARGAIN HUNTERBargain Hunters show a willingness to shop around for the lowest price. This least satisfied shopper executes the shortest store trip (along with Time Stressed) and views the fewest number of displays. Interestingly, they demonstrate the longest average time at shelf, likely due to their discriminating process across price choices. Their register ring is less than average per trip and they are least loyal to the retailer. Bargain Hunters are the most likely user of pre-store media such as circulars to plan their trip and they account for the highest coupon use. This lowest basket spender has the highest non-conversion rate for planned items. Appendix 6 shows the demographic breakout, top impulse categories, and further suggestions on how to engage this shopper segment.

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EMOTIONAL VALENCES BY SHOPPER SEGMENT AND THE IMPLICATIONSMany aspects of emotion are said to be valenced and labeled positive or negative. Indeed, valence is generally considered to be a central feature of emotion, which plays an integral part in how shoppers behave in the retail environment.

In fact, by using time synced EEG brain signals and eye-tracking fixations, POPAI’s Shopper Engagement Study was able to identify emotional valence as a predictor of purchase. Sands Research has developed an Emotional Valence Score (EVS) which measures responses from the Inferior Frontal Gyrus, including right and left hemispheres. The EVS quantifies the magnitude of asymmetrical (right vs. left) activation to index the positive and negative emotion.

The study found that items placed in the cart by a shopper produced a positive emotional brain response. Subsequent eye fixations to the to-be-purchased item demonstrated the purchase intent effect, but it diminished as shoppers spent more time focusing on and approaching the product. This means that the biggest neurological reward is early on in the discovery process. If we think about a food item purchase in terms of the Stimulus-Reward chain, this result points to the importance of early detection. As such, it is all about the first observations of the product and there are a number of action items that flow from this finding.

Retailers and brand marketers need to think about merchandising products more from a distance than up close. Brand manufacturers, understandably, think about their packaging as a single object viewed at a hand-held distance, but as the findings indicate this is a flawed logic and only represents half of the story. Selection of the product and its positive attributes occur at such a distance, that the intricate packaging details are lost on the shopper.

In attaching a magnitude to this effect the EVS was 25, which is much larger than what is seen in other testing scenarios

FIGURE 3Anatomy of a Purchase - Eye-Fixations, Neuro Engagement, & Emotional Valence Score

{ {{

Participant view with eye-tracking

Real time neurological activity

Time synced with eye-tracking, Neuro Engagement Score (NES), and Emotional Valence Score (EVS)

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such as television or print ads. Furthermore, the first fixation effect is more than twice as large as subsequent fixations. Put

another way, viewing a product from a distance is twice as effective as it is up-close at the shelf edge.

This opens up a whole new area of shopper insight research in and of itself. For starters it means there is a baseline now

for what brain signals and responses can predict a purchase. It also means that adjacencies and planograms need to be

reexamined by retailers and brand manufacturers alike to create the optimal selling environment.

Another interesting finding coming out of the EVS is the ability to detect and measure the emotional response shoppers

have between different stores. This allows retailers to rank and compare optimum-shopping environments among their

various banners and formats, as well as how they stack up compared to their competitors. This is a significant finding that if

a retailer is looking to attract and cater to a specific segment they now have a means to better understanding.

Based upon the segmentation model and defining attributes of each segment, Figure 4 shows the emotional valence scores

of each segment. As might be expected, the Explorer segment displays the highest valence score indicating a positive

experience when shopping while Trip Planners exert the lowest valence score denoting negative connotations when it

comes to shopping.

FIGURE 4Emotional Valence Score By Shopper Segment

20

15

10

5

0

-5

-10

-15

-20

Explorer17.0

Bargain Hunter-13.0

Time Stressed -3.6

Trip Planner-15.9

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FIGURE 5Neuro Engagement Score By Shopper Segment

4

5

4.5

3.5

3

2.5

2

1.5

1

0.5

0

Explorer4.3

Bargain Hunter4.3

Time Stressed 3.5

Trip Planner3.1

NEUROLOGICAL ACTIVATION BY SHOPPER SEGMENTThe use of biometric recordings in the marketing environment has been around for decades. These measures include

heart rate, skin conductance, respiration, movement, muscle, pupil dilation and pulse volume. They can be categorized as

peripheral measures of the autonomic nervous system.

These general measurements are useful for gaining a very broad value of activation in the brain. To gain a true sense of

neurological activity, the full spectrum EEG recordings developed by Sands Research delivers objective and empirical

results from the brain’s response to the marketing medium.

Sands Research provided the following Neuro Engagement Score TM (NES), which shows the neurological response of the

shopper to in-store stimuli on a one to five scale (Figure 5). A higher NES equates to an increased level of engagement by

that demographic viewing group. While the emotional valence scores of the segments appear to be all over the board it is

interesting to note that neurologically the segments are firing on all cylinders so to speak and are extremely engaged when

in the retail environment.

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DISCUSSION AND MANAGERIAL IMPLICATIONSMarketers should tie strategy to the profile of these groups with consideration for a more concentrated effort with the Time Stressed and the Explorer segments. A multi-layered global strategy that reaches all four segments may be developed. Tactics should be built around common attributes that resonate among all four groups. Separate, segment specific campaigns may be built for each segment’s unique characteristics by creating visual cues and messaging that communicate targeted benefits to each group’s distinct purchase drivers.

Moreover, retailers and brand manufacturers can utilize this particular segmentation framework to measure and assess how their shopper marketing programs are resonating with each shopper segment. The framework, calculation, and algorithm tools provided set the stage for a common metric that retailers and brand manufacturers can use in developing shopper marketing programs and to measure not only internal success, but to benchmark success against their competitors as they compete for each segmentation of these four shopper types.

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TOP IMPULSE CATEGORIES

TIME STRESSED

21%

AVERAGE AGE: 44

79%

ETHNICITY

GENDER

PRICE SENSITIVITY

TIME PRESSURED

TRIP ORIENTED

CAUCASIAN/WHITE

GUM & MINTS

78%

85%

7%

84%

10%

82%

3%

AFRICAN-AMERICAN/BLACK

SWEET BAKED GOODS-PACKAGED

HISPANIC/LATIN AMERICAN

MARINADE & MISC SAUCE

ASIAN/PACIFIC ISLANDER

For the time-stressed and over-

scheduled shopper, going to the

store is more like hunting than

shopping.

This shopper feels pressure

from not having enough time

and seems to always be in a

hurry. This may explain why

this segment is very unlikely

to utilize time-consuming

pre-store media such as store

circulars or coupons to plan

trips, and demonstrates the least

consistency in using written

lists.

Adding to the time pressures are

perceived budgetary constraints,

although this group is not low

income.

Ensure that the content, packaging, and in-store marketing communications are relevant and engaging. If shoppers are in a supermarket and time pressured, they want to get in and out. Delivering quick solutions and natural adjacencies that are visually appealing and can be understood right away are the key to capturing this segment.

HOW TO ENGAGE THE TIME STRESSED SHOPPER

APPENDIX 4 APPENDIX 3

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EXPLORER

25%

AVERAGE AGE: 50

75%

ETHNICITY

GENDER

PRICE SENSITIVITY

TIME PRESSURED

TRIP ORIENTED

CAUCASIAN/WHITE

DESSERT NOVELTIES - FROZEN

69%

93%

15%

93%

8%

86%

4%


NUTS - SNACK


CRACKERS


For the explorer shopping is an

activity they perform for the

interaction and experience it

provides them.

For many retailers, this is the

largest segment in terms of

traffic, while, at the same time,

they do not represent the largest

percentage of sales.

Although they may not

represent a large percentage

of immediate sales, they are

a real voice for retailers and

brands in the community. Since

they are merely looking for

interaction, they are also very

likely to communicate to others

the experience they had in the

store or with a newly discovered

brand.

Make no mistake, explorers cannot be ignored. But the time spent with them should not be disproportionate to other key segments. The key to engaging this segment is all in range and assortment optimization. Attract and convert explorers by providing information and compelling in-store marketing materials that attract this segment and spark their curiosity during the right trips, helping to maximize the size of their basket by managing the right product and category assortment.

HOW TO ENGAGE THE EXPLORER SHOPPER


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TRIP PLANNER

30%

AVERAGE AGE: 52

70%

ETHNICITY

GENDER

PRICE SENSITIVITY

TIME PRESSURED

TRIP ORIENTED

CAUCASIAN/WHITE

GUM & MINTS

76%

91%

12%

81%

8%

79%

2%


CANDY


ENTREES - SHELF STABLE

ASIAN/PACIFIC ISLANDERFor the trip planner shopping is

all about accomplishing the task

at hand on budget and on time.

This shopper is the most loyal

to retailers, consistently uses

a written list, and is the most

accurate in predicting their total

spend for each shopping trip.

The trip planner identifies as

being controlled and restrained,

which may explain why this

segment purchases the lowest

number of items on impulse;

spends the least amount of time

in-store; and makes fewer trips

per week.

The trip planner has already decided where they go to shop and what particular store will give the best value for their needs. To maximize shopper engagement and conversion, managing the store and aisle layout and experience is critical. This ranges from navigation paths, to shopper centric store and aisle design, as well as more effective use of in-store marketing campaigns. You can also build loyalty and retain this segment by monitoring and managing shopper habits, satisfaction, and banner/brand equity.

HOW TO ENGAGE THE TRIP PLANNER SHOPPER


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BARGAIN HUNTER

25%

AVERAGE AGE: 49

75%

ETHNICITY

GENDER

PRICE SENSITIVITY

TIME PRESSURED

TRIP ORIENTED

CAUCASIAN/WHITE

SWEET BAKED GOODS-PACKAGED

78%

89%

10%

86%

6%

86%

3%


DESSERT NOVELTIES - FROZEN


BREAKFAST - FROZEN


The bargain hunter enjoys

shopping for the thrill of seeking

unusual, unique or ordinarily

expensive goods in unexpected

places at cheaper than normal

prices.

This shopper, defined by his/

her willingness to shop around

for the lowest price, is the least

loyal to retailers and the least

satisfied shopper segment in

overall satisfaction.

The bargain hunter is the most

likely to use pre-store media to

plan their trip having the highest

circular and coupon usage

among the shopper segments.

Paying attention to price is the key to attracting this shopper segment, but savvy retailers and brand marketers alike should recognize that value is the best combination of product, shopping experience and price. Through collaboration retailers and brand marketers can work together to create an impression of a treasure trove, where buyers never know what goodies they’ll find, through specific in-store marketing programs and campaigns.

HOW TO ENGAGE THE BARGAIN HUNTER SHOPPER


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