What’s Up with Pasta

What's Up With Pasta Q1: We need to understand and research why the Spaniards are spending relatively less on Pasta than its European neighbors. Current market research done by AEFPA offers insufficient data, so we need to improve data quality. The main goal is the get a clear demographic segmented market overview. One of the problems is that we cannot clearly identify the potential and current pasta consumers clearly – we simply do not know enough about of core target group. In addition we need insights on consumer behavior and habits as we do not know what drives the consumer decision when choosing pasta and when declining pasta.Another advantage of a broad market study would be that it would become clear if there are segments in the market currently not being explored. As a result we will be able to clearly identify the market entry barriers for pasta. According to our calculations (Appendix 1), there is an underutilized yearly market gap of EUR 87Mln. Given this significan t amount we find it justified to spend 0. 2% (Eur 175. 000) of the market gap initializing the market research plan, collect the data and conduct the analysis.Costs to marketing strategy, marketing planning and implementation are not included in this figure. We estimate the overall cost of the market research will be Eur 132. 800 Judging from informal discussions with contacts in Unilever and Kraft Foods, our estimate seems to be on the low side. Q2 – Methodology: We are interested in conducting both quantitative and qualitative research. In our opinion we need both elements to fully understand the market. This will allow us to better segment the market. Starting point of the quantitative research is the detailed quantitative research already done by AEFPA.The Geographical sales overview, distribution channels and sales pr. pasta type, must be investigated further. We suggest conducting a demographic segmentation overlay to this data, as the segmentation will serve us by divi ding a large population/sample into specific customer groups. We are opting for the demographical segmentation as we expect to receive a large amount of data that otherwise would not be feasible to analyze. Therefore, we cluster the information to make patterns of sub-groups visible and will enable to identify consumer profile and behaviors.We refer to this as top-down market research. The consumer behavior can only be partly captured in the demographic segmentation, so to ensure we have a bulky sample of data, we introduce a bottom-up process by initiating â€Å"Shopper Insights† research. â€Å"Shopper Insights† will in addition to bring to additional data on behavior also provide invaluable insights to the customer’s perception of pasta. The aim with â€Å"Shoppers Insight† is to passively monitor the customer’s behavior in the situation of purchase at point-of-buying to learn about the â€Å"conversion rate†.Unilever defines â€Å"Shopp ers Insight† as † focus on the process that takes place between that first thought the consumer has about purchasing an item, all the way through the selection of that item†. This is further underlined by practical examples from Kraft Foods Switzerland, who has provided access to their methodology to this group. We will be adopting the methods of â€Å"5 S’s† to conduct our â€Å"Shopper insights† research and conduct this across the difference distribution channels mentioned in the case.Detailed explanation in Appendix 2 By making use of both top-down and bottom-up quantitative research, we feel we have adequate data quality. However it is critical to maintain a satisfactory sample size. We assume our sample pool will be the entire Spanish population. There are many considerations when choosing a sampling size. We consider it a tradeoff between costs and sampling quality as there is a linear relationship between the sampling size and the cost. We estimate that the sampling size must be at least 384 people. See further details in appendix 3.To finish the research we introduce â€Å"Consumer Insight† which is a qualitative overlay. Personal interviews with customers will be done immediately after the consumer has been observed in the â€Å"Shoppers Insight†. The sample size when conducting qualitative research is less important as there is no need for statistical significance, so we will be highly selective when choosing participants. Actually we will aim to only interview the â€Å"High-Consumer† and â€Å"Non-consumer† segments found in the top-down demographic segmentation research.This will provide strong qualitative data for creating the marketing strategy and planning. These topics will not be discussed in this paper. Q3 – Implementation: As we want to build in the existing data from AEPFA, significantly more data collecting must be done. We would conduct a survey on a large sample, using these four variables: Age, life-cycle stage (the life cycle stage of a consumer group defines what will be the need of that particular customer), Gender and Income. In addition questions in pasta purchasing history and frequency would be asked.The questions will be designed so the answers can be directly comparable across the entire sample. This can be achieved by having a 1-5 scale designed on which the answers must fit one of the numbers. Example: Question: â€Å"How often do you eat pasta†, Possible answers: â€Å"1: Never, 2: ones a day, 3: ones a week, 4: ones a month, 5: ones a year†. By constructing all questions to fit such answer-schedule, we will be able to achieve statistical significance. The result will be a clear segmented group, where we can establish who are the current consumers (core buying segment) and non-consumers (core anti-buyers).We believe these segments should be targeted for further penetration. Next step we passively and discretely mo nitor the consumer at point-of-buying using the â€Å"5 S’s† approach (See appendix 2). We will be present in all the distribution channels mentioned. This can be done via video or via physical presents. It is paramount the customer is unaware she/he is being monitored as this potentially would influence the buying habits. The consumer segments found above – the consumer and non-consumer – will be specifically targeted in the monitoring. I. e. hen a consumer fits one of the segments, the monitoring will be initiated. We wish to focus on these segments due to costs, but could increase the sampling to all customers across all segments if budget would allow. As the quantitative research should not stand alone, we would initiate in-depth interviews with more open-ended questions to better grasp the motivation behind the choice made by the customer. Such questions could be â€Å"Why did you buy pasta†, â€Å"What type of pasta do you normally buy†, â€Å"why did you buy pasta instead of rice or potatoes. . For the non-consumers questions could be â€Å"Why do you choose rice/potatoes instead of pasta†, †Which pasta products are you missing in the shop† etc. We believe the quantitative and qualitative output of this extensive research plan, by identifying the two interesting segments and dwell into their motivations behind their choice, would form an excellent base for developing an effective market strategy and for creating an overall marketing strategy for Pasta in Spain. ? APPENDIX 1For the calculations of the market gap – difference in current and potential market – we have assumed the following: †¢Current year is 1990. †¢Potential year is 1992. Population has increased by 0. 6% from 1990 to 1992. †¢Euro/Pesetas exchange rate is 166. 386. (Official final fixing when Spain adopted the Euro) †¢Consumer behavior in terms of demand of the different pasta types is unchange d from 1990 – 1992 †¢Pasta price was inflated with 4% from 1990 – 1992. †¢Consumption of pasta rose 1 kg pr. Capita from 1990 – 1992 Pasta Market in 1990: Pasta Market in 1992: ? APPENDIX 2:The 5 S’s method is designed so marketers can observe a customer from entering point-of-buying (POB) to final transaction. The method works on two levels: 1. Consumer level; The consumer are monitored so we follow the target discreetly around the POB. We observe how the consumer Sees, Scans, Spot, Show interest and (potentially) Select the product we represent. This gives us valuable information as we can identifies were in the process we lose the customer (also call Fall-out). The conversation rate is computed as number of consumers selecting our product out of shoppers entering the POB.The net sales for a given company is highly sensitive to changes in conversation rate – Only a small increase in conversion will generate a (relative) large increase i n sales. 2. Store layout and the category placement in POB. We can observe the customers’ ability to find the product in POB; is the product visible to the consumer, where on the shelve is it placed, is it placed with complementary goods? or supplementary goods? After the research is concluded feedback will be delivered to POB to improve visibility if required. ? APPENDIX 3: We recognize the sample size of 1067 is a (very) rough estimate.We opted for an internet resource from Creative Research Systems as we decided to focus our resources on the research planning and method. The sample size is computed using: Confidence level: 95% Confidence Interval (margin of error) 5% Population 40’000’000 We believe these input factors are comparable with real-life statistical simulations. ? APPENDIX 4: As we require a specialized set of data and therefore need a specialized report, we assume such report must be order and bought directly at a Market Research company or instit ute under normal circumstances.As it is specialized we assume the price will be high, so budget with a one-time payment of EUR 75. 000. We have only very little foundation for making this estimate. It was the conclusion of a conversation between marketing executives on Linkedin. The bottom-up research will need to conduct 384 observations in order to fulfill to the minimum sample size requirement found in appendix 3. Based on information from marketing sources at Kraft Foods, we consider it realistic one market researcher can conduct 25 observations in one day. This results in 15. 3 days of work at an assumed daily rate of EUR 1000