The Consumer Lab
The marketing problem
The issue of pricing was bothering John for quite some time. The increasing costs were squeezing the bottom-line, and at the same time aggressive assaults from competition were chiseling at the market share. As the marketing director, he needed to provide a recommendation to the MD on how to take this brand forward. John called up the research agency and took an appointment to spend a day with them exploring the issue and providing clear guidelines for the decision.
The agency’s office was located in a leafy suburb. As he approached the building, it posed an impressive sight. Surrounded by trees was a low rise building, with several satellite dishes dotting its roof. The building looked contemporary, even futuristic, and yet blended well with the surroundings. John was met at the reception by Emily, the Senior Consumer Scientist that he had been talking to for some time. As Emily ushered him to the conference room, John could feel the buzz around the place, a feeling that a rocket was to be launched soon. “Marketing is not rocket science,” it was often said. In fact it was more difficult, he some times felt, before the research agency changed the way his company used consumer and market information.
Learning from the past
The meeting kicked off with Kevin, the modeling expert, bringing up a few charts of the screen. The data Kevin presented looked back at all price increases implemented by John’s company and every major competitor in the past 3 years. The general trend was obvious – the major brands all suffered a temporary set back on price increase, but were able to bounce back in 8-10 weeks to inch back close to their original volume share. There were exceptions, but those were related to other marketing variables, including the response from competition. The Hierarchical Bayesian model showed that if Refresha (the beer brand that John was responsible for) was to increase its price by the required 5%, the brand will show a temporary dip, but would resiliently bounce back.
While john was relieved to know this, he was still concerned about the possibility of opportunistic tactics from competition in this vulnerable moment. Kevin, pulled up the simulator and together with John proceeded to test a number of alternative scenarios of competitive action. Some scenarios worried John and he wasn’t really convinced that the brand will be able to bear the onslaught if the main competitors launched some significant consumer promotions to exploit this opportunity.
In order to bring in an element of the current disposition towards the brand, and to test several additional scenarios, a Discrete Choice Modeling experiment seemed to be in order. Kevin fed the parameters into the computer and tested the design. The field-work department sent out invitations to potential respondents and informed Kevin that he will have something to look at in a few hours.
Analysis of blogs and consumer feeds
A worrier by nature, John also wondered about the adequacy of brand’s strength in the consumer mind. Though the tracking parameters were looking good, John really wanted to see what the consumers are talking about his brand. Emily fixed an appointment with Jenny, the consumer scientist who specialized in studying blogs and bulletin boards, and James whose job was to focus on analyzing consumer behavior video feeds. The agency in collaboration with restaurants, bars, supermarkets, had a continuous flow of live feeds of consumers buying and consuming. Sophisticated software could filter out the feeds where a specific brand was displayed or mentioned.
After a light sandwich in the lunch room, John was back in the meeting room and Jenny and James were waiting for John. James fired up a screen and brought up an image in a restaurant, and a table of a group of friends, ordering beer with their meal. Refresha was on the menu but the group chose its competitor. The picture then moved to a bar, where some college kids seem to be celebrating their survival through another term. Refresha was the drink of choice. While the consumers were too occupied with other concerns and the brand didn’t really come much into the discussion, value for money was mentioned in the context of the brand. John was concerned that this was just the attribute that he would be playing with when he increased the price by 5%. James continued to pull up recordings of consumers using Refresha and competing products. John took copious notes and discussed several questions and hypothesis with the team.
Jenny, came up next and presented her analysis of online consumer conversations. The research company has sophisticated tools not only for analysis of public blogs, but also a panel of specially recruited bloggers for different product categories. The Internet allowed consumers to record much more than their purchases (the old-fashioned diaries and scanner panels were confined to recording consumer purchases) and included what they felt about the brand, how they consumed it (including videos of their consumption situations) and what were their friends taking about it. The analysis came up with a positive picture of the brand. It had a positive word of mouth, was considered to offer good value for money and had an image which resonated well with the younger adults. However, the brand was showing some signs of weariness among the slightly older consumers and some consumers had moved to competing brands. The team felt that while the brand was, overall, in good shape, Refresha should take some specific action targeted at the older consumers, and find more connection points with them.
The final picture
The Discrete Choice Modeling results were in. John was relieved to see that the price elasticity for Refresha in most segments was relatively low. The older consumers again displayed higher elasticity. The Discrete Choice Modeling results also fed into the simulator he was looking at before lunch. The fusion of Discrete Choice Modeling data and market mix modeling gave the simulator an added precision and ability to forecast accurately. He ran several scenarios with the research team and was finally convinced that he could take that price hike but he needed to simultaneously enhance bonding with the relatively older consumers.
The death of the research project
On the way back, John was reflecting on how research had evolved and changed the way his company took marketing decisions. Just a few years ago, it would have taken him more than a month to look at similar data. Gone were the days when he and his team thought in terms of research projects, sample sizes and debated whether they needed to do a research for the particular issue which was facing them. They had to negotiate the price for each project and bargain for the findings to be delivered in 5 weeks, against 6 weeks that the agency wanted. The research project was dead – instead the research company now provided John with a continuous feed of consumer behaviour, attitudes and trends. There was no need to do periodic U&A studies, pricing research, advertising research – the data was all continuously collected by the company and addressing an issue was merely a question of isolating the relevant data and looking at it from the appropriate perspective. The company not only tracked consumer behavior in relation to consumption of specific products, but also general trends in fashion, entertainment, leisure habits and values – providing companies with a holistic picture of the consumer and the way his concerns and focus were moving with time. Of course, once in a while a few additional experiments were necessary – just as the Discrete Choice Modeling that they did for him today. With access to this type of consumer lab enabling him to always keep a finger on the pulse of the consumer, he could react more swiftly, more decisively and be more competitive in the market.
Written by: Ashok Sethi, Head of Consumer Insights for Emerging Markets - TNS
The marketing problem
The issue of pricing was bothering John for quite some time. The increasing costs were squeezing the bottom-line, and at the same time aggressive assaults from competition were chiseling at the market share. As the marketing director, he needed to provide a recommendation to the MD on how to take this brand forward. John called up the research agency and took an appointment to spend a day with them exploring the issue and providing clear guidelines for the decision.
The agency’s office was located in a leafy suburb. As he approached the building, it posed an impressive sight. Surrounded by trees was a low rise building, with several satellite dishes dotting its roof. The building looked contemporary, even futuristic, and yet blended well with the surroundings. John was met at the reception by Emily, the Senior Consumer Scientist that he had been talking to for some time. As Emily ushered him to the conference room, John could feel the buzz around the place, a feeling that a rocket was to be launched soon. “Marketing is not rocket science,” it was often said. In fact it was more difficult, he some times felt, before the research agency changed the way his company used consumer and market information.
Learning from the past
The meeting kicked off with Kevin, the modeling expert, bringing up a few charts of the screen. The data Kevin presented looked back at all price increases implemented by John’s company and every major competitor in the past 3 years. The general trend was obvious – the major brands all suffered a temporary set back on price increase, but were able to bounce back in 8-10 weeks to inch back close to their original volume share. There were exceptions, but those were related to other marketing variables, including the response from competition. The Hierarchical Bayesian model showed that if Refresha (the beer brand that John was responsible for) was to increase its price by the required 5%, the brand will show a temporary dip, but would resiliently bounce back.
While john was relieved to know this, he was still concerned about the possibility of opportunistic tactics from competition in this vulnerable moment. Kevin, pulled up the simulator and together with John proceeded to test a number of alternative scenarios of competitive action. Some scenarios worried John and he wasn’t really convinced that the brand will be able to bear the onslaught if the main competitors launched some significant consumer promotions to exploit this opportunity.
In order to bring in an element of the current disposition towards the brand, and to test several additional scenarios, a Discrete Choice Modeling experiment seemed to be in order. Kevin fed the parameters into the computer and tested the design. The field-work department sent out invitations to potential respondents and informed Kevin that he will have something to look at in a few hours.
Analysis of blogs and consumer feeds
A worrier by nature, John also wondered about the adequacy of brand’s strength in the consumer mind. Though the tracking parameters were looking good, John really wanted to see what the consumers are talking about his brand. Emily fixed an appointment with Jenny, the consumer scientist who specialized in studying blogs and bulletin boards, and James whose job was to focus on analyzing consumer behavior video feeds. The agency in collaboration with restaurants, bars, supermarkets, had a continuous flow of live feeds of consumers buying and consuming. Sophisticated software could filter out the feeds where a specific brand was displayed or mentioned.
After a light sandwich in the lunch room, John was back in the meeting room and Jenny and James were waiting for John. James fired up a screen and brought up an image in a restaurant, and a table of a group of friends, ordering beer with their meal. Refresha was on the menu but the group chose its competitor. The picture then moved to a bar, where some college kids seem to be celebrating their survival through another term. Refresha was the drink of choice. While the consumers were too occupied with other concerns and the brand didn’t really come much into the discussion, value for money was mentioned in the context of the brand. John was concerned that this was just the attribute that he would be playing with when he increased the price by 5%. James continued to pull up recordings of consumers using Refresha and competing products. John took copious notes and discussed several questions and hypothesis with the team.
Jenny, came up next and presented her analysis of online consumer conversations. The research company has sophisticated tools not only for analysis of public blogs, but also a panel of specially recruited bloggers for different product categories. The Internet allowed consumers to record much more than their purchases (the old-fashioned diaries and scanner panels were confined to recording consumer purchases) and included what they felt about the brand, how they consumed it (including videos of their consumption situations) and what were their friends taking about it. The analysis came up with a positive picture of the brand. It had a positive word of mouth, was considered to offer good value for money and had an image which resonated well with the younger adults. However, the brand was showing some signs of weariness among the slightly older consumers and some consumers had moved to competing brands. The team felt that while the brand was, overall, in good shape, Refresha should take some specific action targeted at the older consumers, and find more connection points with them.
The final picture
The Discrete Choice Modeling results were in. John was relieved to see that the price elasticity for Refresha in most segments was relatively low. The older consumers again displayed higher elasticity. The Discrete Choice Modeling results also fed into the simulator he was looking at before lunch. The fusion of Discrete Choice Modeling data and market mix modeling gave the simulator an added precision and ability to forecast accurately. He ran several scenarios with the research team and was finally convinced that he could take that price hike but he needed to simultaneously enhance bonding with the relatively older consumers.
The death of the research project
On the way back, John was reflecting on how research had evolved and changed the way his company took marketing decisions. Just a few years ago, it would have taken him more than a month to look at similar data. Gone were the days when he and his team thought in terms of research projects, sample sizes and debated whether they needed to do a research for the particular issue which was facing them. They had to negotiate the price for each project and bargain for the findings to be delivered in 5 weeks, against 6 weeks that the agency wanted. The research project was dead – instead the research company now provided John with a continuous feed of consumer behaviour, attitudes and trends. There was no need to do periodic U&A studies, pricing research, advertising research – the data was all continuously collected by the company and addressing an issue was merely a question of isolating the relevant data and looking at it from the appropriate perspective. The company not only tracked consumer behavior in relation to consumption of specific products, but also general trends in fashion, entertainment, leisure habits and values – providing companies with a holistic picture of the consumer and the way his concerns and focus were moving with time. Of course, once in a while a few additional experiments were necessary – just as the Discrete Choice Modeling that they did for him today. With access to this type of consumer lab enabling him to always keep a finger on the pulse of the consumer, he could react more swiftly, more decisively and be more competitive in the market.
Written by: Ashok Sethi, Head of Consumer Insights for Emerging Markets - TNS
Labels: analysis of blogs, discrete choice modelling, Hierarchical Bayesian model, Pricing research
posted by Ashok Sethi at 9:40 AM
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